VMware vSphere Big Data Extensions - 2.2 User’s Manual

VMware vSphere Big Data Extensions

Command-Line Interface Guide

vSphere Big Data Extensions 2.2

This document supports the version of each product listed and supports all subsequent versions until the document is replaced by a new edition. To check for more recent editions of this document, see http://www.vmware.com/support/pubs.

EN-001702-00

VMware vSphere Big Data Extensions Command-Line Interface Guide

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http://www.vmware.com/support/

The VMware Web site also provides the latest product updates.

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About This Book 7

Using the Serengeti Remote Command-Line Interface Client 9

Access the Serengeti CLI By Using the Remote CLI Client 9

Managing Application Managers 13

About Application Managers 15

Add an Application Manager by Using the Serengeti Command-Line

Interface 17

View List of Application Managers by using the Serengeti Command-Line

Interface 19

Modify an Application Manager by Using the Serengeti Command-Line

Interface 21

View Supported Distributions for All Application Managers by Using the

Serengeti Command-Line Interface 23

View Configurations or Roles for Application Manager and Distribution by

Using the Serengeti Command-Line Interface 25

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Delete an Application Manager by Using the Serengeti Command-Line

Interface 27

Managing the Big Data Extensions Environment by Using the Serengeti

Command-Line Interface 29

About Application Managers 29

Add a Resource Pool with the Serengeti Command-Line Interface 32

Remove a Resource Pool with the Serengeti Command-Line Interface 33

Add a Datastore with the Serengeti Command-Line Interface 33

Remove a Datastore with the Serengeti Command-Line Interface 33

Add a Network with the Serengeti Command-Line Interface 34

Remove a Network with the Serengeti Command-Line Interface 34

Reconfigure a Static IP Network with the Serengeti Command-Line Interface 35

Reconfigure the DNS Type and Generate a Hostname with the Serengeti Command-Line Interface 35

Increase Cloning Performance and Resource Usage of Virtual Machines 36

VMware vSphere Big Data Extensions Command-Line Interface Guide

Managing Users and User Accounts 39

Create an LDAP Service Configuration File Using the Serengeti Command-Line Interface 39

Activate Centralized User Management Using the Serengeti Command-Line Interface 41

Create a Cluster With LDAP User Authentication Using the Serengeti Command-Line Interface 41

Change User Management Modes Using the Serengeti Command-Line Interface 42

Modify LDAP Configuration Using the Serengeti Command-Line Interface 43

Creating Hadoop and HBase Clusters 45

About Hadoop and HBase Cluster Deployment Types 47

Default Hadoop Cluster Configuration for Serengeti 47

Default HBase Cluster Configuration for Serengeti 48

About Cluster Topology 48

About HBase Clusters 51

About MapReduce Clusters 58

About Data Compute Clusters 61

About Customized Clusters 71

Managing Hadoop and HBase Clusters 79

Stop and Start a Cluster with the Serengeti Command-Line Interface 79

Scale Out a Cluster with the Serengeti Command-Line Interface 80

Scale CPU and RAM with the Serengeti Command-Line Interface 80

Reconfigure a Cluster with the Serengeti Command-Line Interface 81

About Resource Usage and Elastic Scaling 83

Delete a Cluster by Using the Serengeti Command-Line Interface 88

About vSphere High Availability and vSphere Fault Tolerance 88

Reconfigure a Node Group with the Serengeti Command-Line Interface 88

Recover from Disk Failure with the Serengeti Command-Line Interface Client 89

Enter Maintenance Mode to Perform Backup and Restore with the Serengeti Command-Line

Interface Client 89

Monitoring the Big Data Extensions Environment 91

View List of Application Managers by using the Serengeti Command-Line Interface 91

View Available Hadoop Distributions with the Serengeti Command-Line Interface 92

View Supported Distributions for All Application Managers by Using the Serengeti Command-

Line Interface 92

View Configurations or Roles for Application Manager and Distribution by Using the Serengeti

Command-Line Interface 92

View Provisioned Clusters with the Serengeti Command-Line Interface 93

View Datastores with the Serengeti Command-Line Interface 93

View Networks with the Serengeti Command-Line Interface 93

View Resource Pools with the Serengeti Command-Line Interface 94

Cluster Specification Reference 95

Cluster Specification File Requirements 95

Cluster Definition Requirements 95

Annotated Cluster Specification File 96

Cluster Specification Attribute Definitions 99

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White Listed and Black Listed Hadoop Attributes 101

Convert Hadoop XML Files to Serengeti JSON Files 103

Contents

Serengeti CLI Command Reference 105

appmanager Commands 105

cluster Commands 107

connect Command 113

datastore Commands 114

disconnect Command 114

distro list Command 115

mgmtvmcfg Commands 115

network Commands 116

resourcepool Commands 118

topology Commands 118

usermgmt Commands 119

Index 121

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About This Book

VMware vSphere Big Data Extensions Command-Line Interface Guide describes how to use the Serengeti Command-Line Interface (CLI) to manage the vSphere resources that you use to create Hadoop and HBase clusters, and how to create, manage, and monitor Hadoop and HBase clusters with the VMware Serengeti™ CLI.

VMware vSphere Big Data Extensions Command-Line Interface Guide also describes how to perform Hadoop and HBase operations with the Serengeti CLI, and provides cluster specification and Serengeti CLI command references.

Intended Audience

This guide is for system administrators and developers who want to use Serengeti to deploy and manage Hadoop clusters. To successfully work with Serengeti, you should be familiar with Hadoop and VMware vSphere®.

VMware Technical Publications Glossary

VMware Technical Publications provides a glossary of terms that might be unfamiliar to you. For definitions of terms as they are used in VMware technical documentation, go to

http://www.vmware.com/support/pubs.

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Using the Serengeti Remote

Command-Line Interface Client 1

The Serengeti Remote Command-Line Interface Client lets you access the Serengeti Management Server to deploy, manage, and use Hadoop.

This chapter includes the following topics:

“Access the Serengeti CLI By Using the Remote CLI Client,” on page 9

“Log in to Hadoop Nodes with the Serengeti Command-Line Interface Client,” on page 10

Access the Serengeti CLI By Using the Remote CLI Client

You can access the Serengeti Command-Line Interface (CLI) to perform Serengeti administrative tasks with the Serengeti Remote CLI Client.

Prerequisites

Use the VMware vSphere Web Client to log in to the VMware vCenter Server® on which you deployed

the Serengeti vApp.

Verify that the Serengeti vApp deployment was successful and that the Management Server is running.

Verify that you have the correct password to log in to Serengeti CLI. See the VMware vSphere Big Data

Extensions Administrator's and User's Guide.

The Serengeti CLI uses its vCenter Server credentials.

Verify that the Java Runtime Environment (JRE) is installed in your environment and that its location is

in your path environment variable.

Procedure

1 Download the Serengeti CLI package from the Serengeti Management Server.

Open a Web browser and navigate to the following URL: https://server_ip_address/cli/VMware-

Serengeti-CLI.zip

2 Download the ZIP file.

The filename is in the format VMware-Serengeti-cli-version_number-build_number.ZIP.

3 Unzip the download.

The download includes the following components.

The serengeti-cli-version_number JAR file, which includes the Serengeti Remote CLI Client.

The samples directory, which includes sample cluster configurations.

Libraries in the lib directory.

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4 Open a command shell, and change to the directory where you unzipped the package.

5 Change to the cli directory, and run the following command to enter the Serengeti CLI.

For any language other than French or German, run the following command.

java -jar serengeti-cli-version_number.jar

For French or German languages, which use code page 850 (CP 850) language encoding when

running the Serengeti CLI from a Windows command console, run the following command.

java -Dfile.encoding=cp850 -jar serengeti-cli-version_number.jar

6 Connect to the Serengeti service.

You must run the connect host command every time you begin a CLI session, and again after the 30 minute session timeout. If you do not run this command, you cannot run any other commands.

a Run the connect command.

connect --host xx.xx.xx.xx:8443

b At the prompt, type your user name, which might be different from your login credentials for the

Serengeti Management Server.

NOTE If you do not create a user name and password for the Serengeti Command-Line Interface Client, you can use the default vCenter Server administrator credentials. The Serengeti Command-Line Interface Client uses the vCenter Server login credentials with read permissions on the Serengeti Management Server.

c At the prompt, type your password.

A command shell opens, and the Serengeti CLI prompt appears. You can use the help command to get help with Serengeti commands and command syntax.

To display a list of available commands, type help.

To get help for a specific command, append the name of the command to the help command.

help cluster create

Press Tab to complete a command.

Log in to Hadoop Nodes with the Serengeti Command-Line Interface Client

To perform troubleshooting or to run your management automation scripts, log in to Hadoop master, worker, and client nodes with SSH from the Serengeti Management Server using SSH client tools such as SSH, PDSH, ClusterSSH, and Mussh, which do not require password authentication.

To connect to Hadoop cluster nodes over SSH, you can use a user name and password authenticated login. All deployed nodes are password-protected with either a random password or a user-specified password that was assigned when the cluster was created.

Prerequisites

Use the vSphere Web Client to log in to vCenter Server, and verify that the Serengeti Management Server virtual machine is running.

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Chapter 1 Using the Serengeti Remote Command-Line Interface Client

Procedure

1 Right-click the Serengeti Management Server virtual machine and select Open Console.

The password for the Serengeti Management Server appears.

NOTE If the password scrolls off the console screen, press Ctrl+D to return to the command prompt.

2 Use the vSphere Web Client to log in to the Hadoop node.

The password for the root user appears on the virtual machine console in the vSphere Web Client.

3 Change the password of the Hadoop node by running the set-password -u command.

sudo /opt/serengeti/sbin/set-password -u

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Managing Application Managers 2

A key to managing your Hadoop clusters is understanding how to manage the different application managers that you use in your Big Data Extensions environment.

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About Application Managers 3

You can use Cloudera Manager, Ambari, and the default application manager to provision and manage clusters with VMware vSphere Big Data Extensions.

After you add a new Cloudera Manager or Ambari application manager to Big Data Extensions, you can redirect your software management tasks, including monitoring and managing clusters, to that application manager.

You can use an application manager to perform the following tasks:

List all available vendor instances, supported distributions, and configurations or roles for a specific

application manager and distribution.

Create clusters.

Monitor and manage services from the application manager console.

Check the documentation for your application manager for tool-specific requirements.

Restrictions

The following restrictions apply to Cloudera Manager and Ambari application managers:

To add an application manager with HTTPS, use the FQDN instead of the URL.

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You cannot rename a cluster that was created with a Cloudera Manager or Ambari application

manager.

You cannot change services for a big data cluster from Big Data Extensions if the cluster was created

with Ambari or Cloudera Manager application manager.

To change services, configurations, or both, you must make the changes from the application manager

on the nodes.

If you install new services, Big Data Extensions starts and stops the new services together with old services.

If you use an application manager to change services and big data cluster configurations, those changes

cannot be synced from Big Data Extensions. The nodes that you create with Big Data Extensions do not contain the new services or configurations.

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Add an Application Manager by Using the Serengeti Command-Line

Interface 4

To use either Cloudera Manager or Ambari application managers, you must add the application manager and add server information to Big Data Extensions.

NOTE If you want to add a Cloudera Manager or Ambari application manager with HTTPS, use the FQDN in place of the URL.

Procedure

1 Access the Serengeti CLI.

2 Run the appmanager add command.

appmanager add --name application_manager_name --type [ClouderaManager|Ambari]

--url http[s]://server:port

Application manager names can include only alphanumeric characters ([0-9, a-z, A-Z]) and the following special characters; underscores, hyphens, and blank spaces.

You can use the optional description variable to include a description of the application manager instance.

3 Enter your username and password at the prompt.

4 If you specified SSL, enter the file path of the SSL certificate at the prompt.

What to do next

To verify that the application manager was added successfully, run the appmanager list command.

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View List of Application Managers by using the Serengeti Command-Line

Interface 5

You can use the appManager list command to list the application managers that are installed on the Big Data Extensions environment.

Prerequisites

Verify that you are connected to an application manager.

Procedure

1 Access the Serengeti CLI.

2 Run the appmanager list command.

appmanager list

The command returns a list of all application managers that are installed on the Big Data Extensions environment.

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Modify an Application Manager by Using the Serengeti Command-Line

Interface 6

You can modify the information for an application manager with the Serengeti CLI, for example, you can change the manager server IP address if it is not a static IP, or you can upgrade the administrator account.

Prerequisites

Verify that you have at least one external application manager installed on your Big Data Extensions environment.

Procedure

1 Access the Serengeti CLI.

2 Run the appmanager modify command.

appmanager modify --name application_manager_name

--url <http[s]://server:port>

Additional parameters are available for this command. For more information about this command, see

“appmanager modify Command,” on page 106.

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View Supported Distributions for All Application Managers by Using the

Serengeti Command-Line Interface 7

Supported distributions are those distributions that are supported by Big Data Extensions. Available distributions are those distributions that have been added into your Big Data Extensions environment. You can view a list of the Hadoop distributions that are supported in the Big Data Extensions environment to determine if a particular distribution is available for a particular application manager.

Prerequisites

Verify that you are connected to an application manager.

Procedure

1 Access the Serengeti CLI.

2 Run the appmanager list command.

appmanager list --name application_manager_name [--distros]

If you do not include the --name parameter, the command returns a list of all the Hadoop distributions that are supported on each of the application managers in the Big Data Extensions environment.

The command returns a list of all distributions that are supported for the application manager of the name that you specify.

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View Configurations or Roles for Application Manager and Distribution by Using the Serengeti Command-

Line Interface 8

You can use the appManager list command to list the Hadoop configurations or roles for a specific application manager and distribution.

The configuration list includes those configurations that you can use to configure the cluster in the cluster specifications.

The role list contains the roles that you can use to create a cluster. You should not use unsupported roles to create clusters in the application manager.

Prerequisites

Verify that you are connected to an application manager.

Procedure

1 Access the Serengeti CLI.

2 Run the appmanager list command.

appmanager list --name application_manager_name [--distro distro_name (--configurations | --roles) ]

The command returns a list of the Hadoop configurations or roles for a specific application manager and distribution.

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Delete an Application Manager by Using the Serengeti Command-Line

Interface 9

You can use the Serengeti CLI to delete an application manager when you no longer need it.

Prerequisites

Verify that you have at least one external application manager installed on your Big Data Extensions

environment.

Verify that application manager you want to delete does not contain any clusters, or the deletion

process will fail.

Procedure

1 Access the Serengeti CLI.

2 Run the appmanager delete command.

appmanager delete --name application_manager_name

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Managing the Big Data Extensions Environment by Using the Serengeti

Command-Line Interface 10

You must manage yourBig Data Extensions, which includes ensuring that if you choose not to add the resource pool, datastore, and network when you deploy the Serengeti vApp, you add the vSphere resources before you create a Hadoop or HBase cluster. You must also add additional application managers, if you want to use either Ambari or Cloudera Manager to manage your Hadoop clusters. You can remove resources that you no longer need.

This chapter includes the following topics:

“About Application Managers,” on page 29

“Add a Resource Pool with the Serengeti Command-Line Interface,” on page 32

“Remove a Resource Pool with the Serengeti Command-Line Interface,” on page 33

“Add a Datastore with the Serengeti Command-Line Interface,” on page 33

“Remove a Datastore with the Serengeti Command-Line Interface,” on page 33

“Add a Network with the Serengeti Command-Line Interface,” on page 34

“Remove a Network with the Serengeti Command-Line Interface,” on page 34

“Reconfigure a Static IP Network with the Serengeti Command-Line Interface,” on page 35

“Reconfigure the DNS Type and Generate a Hostname with the Serengeti Command-Line Interface,”

on page 35

“Increase Cloning Performance and Resource Usage of Virtual Machines,” on page 36

About Application Managers

You can use Cloudera Manager, Ambari, and the default application manager to provision and manage clusters with VMware vSphere Big Data Extensions.

You can use an application manager to perform the following tasks:

List all available vendor instances, supported distributions, and configurations or roles for a specific

application manager and distribution.

Create clusters.

Monitor and manage services from the application manager console.

Check the documentation for your application manager for tool-specific requirements.

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Restrictions

The following restrictions apply to Cloudera Manager and Ambari application managers:

To add an application manager with HTTPS, use the FQDN instead of the URL.

You cannot rename a cluster that was created with a Cloudera Manager or Ambari application

manager.

You cannot change services for a big data cluster from Big Data Extensions if the cluster was created

with Ambari or Cloudera Manager application manager.

To change services, configurations, or both, you must make the changes from the application manager

on the nodes.

If you install new services, Big Data Extensions starts and stops the new services together with old services.

If you use an application manager to change services and big data cluster configurations, those changes

cannot be synced from Big Data Extensions. The nodes that you create with Big Data Extensions do not contain the new services or configurations.

Add an Application Manager by Using the Serengeti Command-Line Interface

To use either Cloudera Manager or Ambari application managers, you must add the application manager and add server information to Big Data Extensions.

NOTE If you want to add a Cloudera Manager or Ambari application manager with HTTPS, use the FQDN in place of the URL.

Procedure

1 Access the Serengeti CLI.

2 Run the appmanager add command.

appmanager add --name application_manager_name --type [ClouderaManager|Ambari]

--url http[s]://server:port

Application manager names can include only alphanumeric characters ([0-9, a-z, A-Z]) and the following special characters; underscores, hyphens, and blank spaces.

You can use the optional description variable to include a description of the application manager instance.

3 Enter your username and password at the prompt.

4 If you specified SSL, enter the file path of the SSL certificate at the prompt.

What to do next

To verify that the application manager was added successfully, run the appmanager list command.

Modify an Application Manager by Using the Serengeti Command-Line Interface

Prerequisites

Verify that you have at least one external application manager installed on your Big Data Extensions environment.

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Chapter 10 Managing the Big Data Extensions Environment by Using the Serengeti Command-Line Interface

Procedure

1 Access the Serengeti CLI.

2 Run the appmanager modify command.

appmanager modify --name application_manager_name

--url <http[s]://server:port>

Additional parameters are available for this command. For more information about this command, see

“appmanager modify Command,” on page 106.

View Supported Distributions for All Application Managers by Using the Serengeti Command-Line Interface

Prerequisites

Verify that you are connected to an application manager.

Procedure

1 Access the Serengeti CLI.

2 Run the appmanager list command.

appmanager list --name application_manager_name [--distros]

If you do not include the --name parameter, the command returns a list of all the Hadoop distributions that are supported on each of the application managers in the Big Data Extensions environment.

The command returns a list of all distributions that are supported for the application manager of the name that you specify.

View Configurations or Roles for Application Manager and Distribution by Using the Serengeti Command-Line Interface

You can use the appManager list command to list the Hadoop configurations or roles for a specific application manager and distribution.

The configuration list includes those configurations that you can use to configure the cluster in the cluster specifications.

The role list contains the roles that you can use to create a cluster. You should not use unsupported roles to create clusters in the application manager.

Prerequisites

Verify that you are connected to an application manager.

Procedure

1 Access the Serengeti CLI.

2 Run the appmanager list command.

appmanager list --name application_manager_name [--distro distro_name (--configurations | --roles) ]

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The command returns a list of the Hadoop configurations or roles for a specific application manager and distribution.

View List of Application Managers by using the Serengeti Command-Line Interface

You can use the appManager list command to list the application managers that are installed on the Big Data Extensions environment.

Prerequisites

Verify that you are connected to an application manager.

Procedure

1 Access the Serengeti CLI.

2 Run the appmanager list command.

appmanager list

The command returns a list of all application managers that are installed on the Big Data Extensions environment.

Delete an Application Manager by Using the Serengeti Command-Line Interface

You can use the Serengeti CLI to delete an application manager when you no longer need it.

Prerequisites

Verify that you have at least one external application manager installed on your Big Data Extensions

environment.

Verify that application manager you want to delete does not contain any clusters, or the deletion

process will fail.

Procedure

1 Access the Serengeti CLI.

2 Run the appmanager delete command.

appmanager delete --name application_manager_name

Add a Resource Pool with the Serengeti Command-Line Interface

You add resource pools to make them available for use by Hadoop clusters. Resource pools must be located at the top level of a cluster. Nested resource pools are not supported.

When you add a resource pool to Big Data Extensions it symbolically represents the actual vSphere resource pool as recognized by vCenter Server. This symbolic representation lets you use the Big Data Extensions resource pool name, instead of the full path of the resource pool in vCenter Server, in cluster specification files.

NOTE After you add a resource pool to Big Data Extensions, do not rename the resource pool in vSphere. If you rename it, you cannot perform Serengeti operations on clusters that use that resource pool.

Procedure

1 Access the Serengeti Command-Line Interface client.

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2 Run the resourcepool add command.

The --vcrp parameter is optional.

This example adds a Serengeti resource pool named myRP to the vSphere rp1 resource pool that is contained by the cluster1 vSphere cluster.

resourcepool add --name myRP --vccluster cluster1 --vcrp rp1

Remove a Resource Pool with the Serengeti Command-Line Interface

You can remove resource pools from Serengeti that are not in use by a Hadoop cluster. You remove resource pools when you do not need them or if you want the Hadoop clusters you create in the Serengeti Management Server to be deployed under a different resource pool. Removing a resource pool removes its reference in vSphere. The resource pool is not deleted.

Procedure

1 Access the Serengeti Command-Line Interface client.

2 Run the resourcepool delete command.

If the command fails because the resource pool is referenced by a Hadoop cluster, you can use the

resourcepool list command to see which cluster is referencing the resource pool.

This example deletes the resource pool named myRP.

resourcepool delete --name myRP

Add a Datastore with the Serengeti Command-Line Interface

You can add shared and local datastores to the Serengeti server to make them available to Hadoop clusters.

Procedure

1 Access the Serengeti CLI.

2 Run the datastore add command.

This example adds a new, local storage datastore named myLocalDS. The value of the --spec parameter,

local*, is a wildcard specifying a set of vSphere datastores. All vSphere datastores whose names begin

with “local” are added and managed as a whole by Serengeti.

datastore add --name myLocalDS --spec local* --type LOCAL

What to do next

After you add a datastore to Big Data Extensions, do not rename the datastore in vSphere. If you rename it, you cannot perform Serengeti operations on clusters that use that datastore.

Remove a Datastore with the Serengeti Command-Line Interface

You can remove any datastore from Serengeti that is not referenced by any Hadoop clusters. Removing a datastore removes only the reference to the vCenter Server datastore. The datastore itself is not deleted.

You remove datastores if you do not need them or if you want to deploy the Hadoop clusters that you create in the Serengeti Management Server under a different datastore.

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Procedure

1 Access the Serengeti CLI.

2 Run the datastore delete command.

If the command fails because the datastore is referenced by a Hadoop cluster, you can use the datastore

list command to see which cluster is referencing the datastore.

This example deletes the myDS datastore.

datastore delete --name myDS

Add a Network with the Serengeti Command-Line Interface

You add networks to Big Data Extensions to make their IP addresses available to Hadoop clusters. A network is a port group, as well as a means of accessing the port group through an IP address.

After you add a network to Big Data Extensions, do not rename it in vSphere. If you rename the network, you cannot perform Serengeti operations on clusters that use that network.

Prerequisites

If your network uses static IP addresses, be sure that the addresses are not occupied before you add the network.

Procedure

1 Access the Serengeti CLI.

2 Run the network add command.

This example adds a network named myNetwork to the 10PG vSphere port group. Virtual machines that use this network use DHCP to obtain the IP addresses.

network add --name myNetwork --portGroup 10PG --dhcp

This example adds a network named myNetwork to the 10PG vSphere port group. Hadoop nodes use addresses in the 192.168.1.2-100 IP address range, the DNS server IP address is 10.111.90.2, the gateway address is 192.168.1.1, and the subnet mask is 255.255.255.0.

network add --name myNetwork --portGroup 10PG --ip 192.168.1.2-100 --dns 10.111.90.2

--gateway 192.168.1.1 --mask 255.255.255.0

To specify multiple IP address segments, use multiple strings to express the IP address range in the format xx.xx.xx.xx-xx[,xx]*.

xx.xx.xx.xx-xx, xx.xx.xx.xx-xx, single_ip, single_ip

This example adds a dynamic network with DHCP assigned IP addresses and meaningful host name.

network add --name ddnsNetwork --dhcp --portGroup pg1 --dnsType DYNAMIC --generateHostname true

Remove a Network with the Serengeti Command-Line Interface

You can remove networks from Serengeti that are not referenced by any Hadoop clusters. Removing an unused network frees the IP addresses for reuse.

Procedure

1 Access the Serengeti CLI.

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2 Run the network delete command.

network delete --name network_name

If the command fails because the network is referenced by a Hadoop cluster, you can use the network

list --detail command to see which cluster is referencing the network.

Reconfigure a Static IP Network with the Serengeti Command-Line Interface

You can reconfigure a Serengeti static IP network by adding IP address segments to it. You might need to add IP address segments so that there is enough capacity for a cluster that you want to create.

If the IP range that you specify includes IP addresses that are already in the network, Serengeti ignores the duplicated addresses. The remaining addresses in the specified range are added to the network. If the network is already used by a cluster, the cluster can use the new IP addresses after you add them to the network. If only part of the IP range is used by a cluster, the unused IP address can be used when you create a new cluster.

Prerequisites

If your network uses static IP addresses, be sure that the addresses are not occupied before you add the network.

Procedure

1 Access the Serengeti CLI.

2 Run the network modify command.

This example adds IP addresses from 192.168.1.2 to 192.168.1.100 to a network named myNetwork.

network modify --name myNetwork --addIP 192.168.1.2-100

Reconfigure the DNS Type and Generate a Hostname with the Serengeti Command-Line Interface

You can reconfigure a network's Domain Name System (DNS) type, and specify that Big Data Extensions generate meaningful host names for the nodes in a Hadoop cluster.

After you add a network to Big Data Extensions, do not rename it in vSphere. If you rename the network, you cannot perform Serengeti operations on clusters that use that network.

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There are three DNS options you can specify:

Normal

Dynamic

Others

Host names provide easier visual identification, as well as allowing you to use services such as Single SignOn, which requires the use of a properly configured DNS.

The value of --generateHostname option can be true or false. If the DNS type you specify is Dynamic or Others, this value defaults to true, even if you do not specify the --generateHostname option. If the DNS type you specify is Normal, you can specify that Big Data Extensions use either a true or false value with the

--generateHostname option.

The DNS server provides both forward and reverse FQDN to IP resolution. Reverse DNS is IP address to domain name mapping. The opposite of forward (normal) DNS which maps domain names to IP addresses. Normal is the default DNS type.

Dynamic DNS (DDNS or DynDNS) is a method of automatically updating a name server in the Domain Name System (DNS) with the active DNS configuration of its configured hostnames, addresses or other information. Big Data Extensions integrates with a Dynamic DNS server in its network through which it provides meaningful host names to the nodes in a Hadoop cluster. . The cluster will then automatically register with the DNS server.

There is no DNS server, or the DNS server doesn't provide normal DNS resolution or Dynamic DNS services. In this case, you must add FQDN/IP mapping for all nodes in the /etc/hosts file for each node in the cluster. Through this mapping of hostnames to IP addresses each node can contact another node in the cluster.

Procedure

1 Access the Serengeti CLI.

2 Run the network modify command.

There are three DNS types you can specify: NORMAL, DYNAMIC, and OTHERS. NORMAL is the default value.

This example modifies a network named myNetwork to use a Dynamic DNS type. Virtual machines that use this network will use DHCP to obtain the IP addresses.

network modify --name myNetwork --dnsType DYNAMIC --generateHostname true

Increase Cloning Performance and Resource Usage of Virtual Machines

You can rapidly clone and deploy virtual machines using Instant Clone, a feature of vSphere 6.0. Instant Clone is enabled by default when using use vSphere 6.0 to run your Big Data Extensions deployment.

Using Instant Clone, a parent virtual machine is forked, and then a child virtual machine (or instant clone) is created. The child virtual machine leverages the storage and memory of the parent, reducing resource usage.

When provisioning a cluster, Big Data Extensions creates a parent virtual machine for each host on which a cluster node has been placed. After provisioning a new resource pool labeled $serengeti.uuid-

BigDataExtension-InstantCloneParentVMs is visible in vCenter Server. This resource pool contains several

parent virtual machines. Normal cluster nodes are instantly cloned from these parent virtual machines. Once the parent virtual machines are created on the cluster hosts, the time required to provision and scale a cluster is significantly reduced.

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When scaling a cluster the clone type you specified during cluster creation continues to be used, regardless of what the current clone type is. For example, if you create a cluster using instant clone, then change your Big Data Extensions clone type to fast clone, the cluster you provisioned using instant clone will continue to use instant clone to scale out the cluster.

If you create clusters and later want to make changes to the template virtual machine used to provision those clusters, you must first delete all the existing parent virtual machines before using the new template virtual machine. When you create clusters using the new template, Big Data Extensions creates new parent virtual machines based on the new template.

Prerequisites

Your Big Data Extensions deployment must use vSphere 6.0 to take advantage of Instant Clone.

Procedure

1 Log into the Serengeti Management Server.

2 Edit the /opt/serengeti/conf/serengeti.properties file and change the value of

cluster.clone.service=fast.

The default clone type when running vSphere 6.0 is Instant Clone.

cluster.clone.service = fast

3 To enable Fast Clone, restart the Serengeti Management Server .

sudo /sbin/service tomcat restart

The Serengeti Management Server reads the revisedserengeti.properties file and applies the Fast Clone feature to all new clusters you create.

What to do next

All clusters you create will now use Fast Clone to deploy virtual machines. See Chapter 12, “Creating

Hadoop and HBase Clusters,” on page 45.

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Managing Users and User Accounts 11

By default Big Data Extensions provides authentication only for local user accounts. If you want to use LDAP (either Active Directory or an OpenLDAP compatible directory) to authenticate users, you must configure Big Data Extensions for use with your LDAP or Active Directory service.

This chapter includes the following topics:

“Create an LDAP Service Configuration File Using the Serengeti Command-Line Interface,” on

page 39

“Activate Centralized User Management Using the Serengeti Command-Line Interface,” on page 41

“Create a Cluster With LDAP User Authentication Using the Serengeti Command-Line Interface,” on

page 41

“Change User Management Modes Using the Serengeti Command-Line Interface,” on page 42

“Modify LDAP Configuration Using the Serengeti Command-Line Interface,” on page 43

Create an LDAP Service Configuration File Using the Serengeti Command-Line Interface

Create a configuration file that identifies your LDAP or Active Directory server environment.

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Prerequisites

Deploy the Serengeti vApp.

Ensure that you have adequate resources allocated to run the Hadoop cluster.

To use any Hadoop distribution other than the default distribution, add one or more Hadoop

distributions. See the VMware vSphere Big Data Extensions Administrator's and User's Guide.

Procedure

1 Access the Serengeti CLI.

2 Navigate to a directory on the Serengeti Management Server where you want to create and store the

configuration file.

You can use the directory /opt/serengeti/etc to store your configuration file.

VMware vSphere Big Data Extensions Command-Line Interface Guide

3 Using a text editor, create a JavaScript Object Notation (JSON) file containing the configuration settings

for your LDAP or Active Directory service.

The format of the configuration file is shown below.

{ "type": "user_mode_type", "primaryUrl": "ldap://AD_LDAP_server_IP_address:network_port", "baseUserDn": "DN_information", "baseGroupDn": "DN_information", "userName": "username", "password": "password", "mgmtVMUserGroupDn":"DN_information" }

Table 11‑1. LDAP Connection Information

type The external user authentication service you will use, which is either AD_AS_LDAP or LDAP.

baseUserDn

baseGroupDn

primaryUrl

mgmtVMUserGroupDn

userName

password

Specify the base user DN.

Specify the base group DN.

Specify the primary server URL of your Active Directory or LDAP server.

(Optional) Specify the base DN for searching groups to access the Serengeti Management Server.

Type the username of the Active Directory or LDAP server administrator account.

Type the password of the Active Directory or LDAP server administrator account.

4 When you complete the file, save your work.

Example: Example LDAP Configuration File

The following example illustrates the configuration file for an LDAP server within the acme.com domain.

{ "type": "LDAP", "primaryUrl": "ldap://acme.com:8888", "baseUserDn": "ou=users,dc=dev,dc=acme,dc=com", "baseGroupDn": "ou=users,dc=dev,dc=acme,dc=com", "userName": "jsmith", "password": "MyPassword", "mgmtVMUserGroupDn":"cn=Administrators,cn=Builtin,dc=dev,dc=acme,dc=com" }

What to do next

With an LDAP configuration file created, you can now activate centralized user management for your Big Data Extensions environment. See “Activate Centralized User Management Using the Serengeti

Command-Line Interface,” on page 41.

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Chapter 11 Managing Users and User Accounts

Activate Centralized User Management Using the Serengeti Command-Line Interface

You must specify that Big Data Extensions use an external user identity source before you can manage users through your LDAP or Active Directory.

Prerequisites

Deploy the Serengeti vApp.

Ensure that you have adequate resources allocated to run the Hadoop cluster.

To use any Hadoop distribution other than the default distribution, add one or more Hadoop

distributions. See the VMware vSphere Big Data Extensions Administrator's and User's Guide.

Create a configuration file identifying your LDAP or Active Directory environment for use with

Big Data Extensions. See “Create an LDAP Service Configuration File Using the Serengeti Command-

Line Interface,” on page 39

Procedure

1 Access the Serengeti CLI.

2 Run the command usermgmtserver add --cfgfile config_file_path

This example activates centralized user management, specifying the file /opt/serengeti/LDAPConfigFile.cfg as the file containing your LDAP configuration settings.

usermgmtserver add --cfgfile /opt/serengeti/LDAPConfigFile.cfg

3 Run the mgmtvmcfg get to verify successful configuration of your environment by printing out the

LDAP or Active Directory configuration information.

The contents of the active configuration file in use by your Big Data Extensions environment prints to the terminal.

What to do next

When you activate centralized user management, you can create clusters and assign user management to roles using the users and user groups defined by your LDAP or Active Directory service. See “Create a

Cluster With LDAP User Authentication Using the Serengeti Command-Line Interface,” on page 41.

Create a Cluster With LDAP User Authentication Using the Serengeti Command-Line Interface

With centralized user management configured and activated, you can grant privileges to users and user groups in your LDAP or Active Directory service to individual Hadoop clusters that you create.

As an example of how you can use centralized user management in your Big Data Extensions environment, you can assign groups with administrative privileges in your LDAP or Active Directory service access to the Serengeti Management Server. This allows those users to administer Big Data Extensions and the Serengeti Management Server. You can then give another user group access to Hadoop cluster nodes, allowing them to run Hadoop jobs.

To access the Serengeti CLI and Serengeti commands, users must change to the user serengeti after they login. For example, you can use the command su to change to the serengeti user, after which you can access the Serengeti CLI.

Prerequisites

Deploy the Serengeti vApp.

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Ensure that you have adequate resources allocated to run the Hadoop cluster.

To use any Hadoop distribution other than the default distribution, add one or more Hadoop

distributions. See the VMware vSphere Big Data Extensions Administrator's and User's Guide.

Activate centralized user management for your Big Data Extensions deployment. See “Activate

Centralized User Management Using the Serengeti Command-Line Interface,” on page 41.

Procedure

1 Access the Serengeti CLI.

2 Run the cluster create command, and specify the value of the --adminGroupName parameter and --

userGroupName parameter using the names of administrative groups and user groups to whom you

want to grant privileges for the cluster you are creating.

cluster create --name cluster_name --type hbase --adminGroupName AdminGroupName -userGroupName UserGroupName

What to do next

After you deploy the cluster, you can access the Hadoop cluster by using several methods. See the VMware vSphere Big Data Extensions Administrator's and User's Guide.

Change User Management Modes Using the Serengeti Command-Line Interface

You can change the user management mode of your Big Data Extensions environment. You can choose to use local user management, LDAP, or a combination of the two.

Big Data Extensions lets you authenticate local users, those managed by LDAP or Active Directory, or a combination of these authentication methods.

Table 11‑2. User Authentication Modes

User Mode Description

Local

LDAP user

Mixed mode

Prerequisites

Deploy the Serengeti vApp.

Ensure that you have adequate resources allocated to run the Hadoop cluster.

Specify LOCAL to create and manage users and groups that are stored locally in your Big Data Extensions environment. Local is the default user management solution.

Specify LDAP to create and manage users and groups that are stored in your organization's identity source, such as Active Directory or LDAP. If you choose LDAP user you must configure Big Data Extensions to use an LDAP or Active Directory service (Active Directory as LDAP).

Specify MIXED to use a combination of both local users and users stored in an external identity source. If you choose mixed mode you must configure Big Data Extensions to use an LDAP or Active Directory service (Active Directory as LDAP).

To use any Hadoop distribution other than the default distribution, add one or more Hadoop

distributions. See the VMware vSphere Big Data Extensions Administrator's and User's Guide.

Procedure

1 Access the Serengeti CLI.

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Chapter 11 Managing Users and User Accounts

2 Run the command mgmtvmcfg modify to specify the user authentication mode you want to use.

Specify LOCAL to create and manage users and groups that are stored locally in your

Big Data Extensions environment. LOCAL is the default user management solution when no Active Directory or LDAP service is available.

mgmtvmcfg modify LOCAL

Specify MIXED to use a combination of both local users and users stored in an external identity

source. If you choose mixed mode you must configure Big Data Extensions to use an LDAP or Active Directory service.

mgmtvmcfg modify MIXED

Specify LDAP to create and manage users and groups that are stored in your organization's identity

source, such as Active Directory as LDAP or LDAP. If you use LDAP you must configure Big Data Extensions to use an LDAP or Active Directory service.

mgmtvmcfg modify LDAP

Big Data Extensions uses the user authentication mode you specify.

Modify LDAP Configuration Using the Serengeti Command-Line Interface

You can modify your LDAP settings and make those changes available to your Big Data Extensions environment.

You can populate changes you make to your LDAP configuration settings to Big Data Extensions. This lets you update your LDAP service information.

Prerequisites

Deploy the Serengeti vApp.

Ensure that you have adequate resources allocated to run the Hadoop cluster.

To use any Hadoop distribution other than the default distribution, add one or more Hadoop

distributions. See the VMware vSphere Big Data Extensions Administrator's and User's Guide.

Activate centralized user management for your Big Data Extensions deployment. See “Activate

Centralized User Management Using the Serengeti Command-Line Interface,” on page 41.

Modify the LDAP configuration file to account for any changes you want to make to your user

management settings. See “Create an LDAP Service Configuration File Using the Serengeti Command-

Line Interface,” on page 39

Procedure

1 Access the Serengeti CLI.

2 Run the command usermgmtserver modify --cfgfile config_file_path

usermgmtserver modify --cfgfile config_file_path

Any changes you made to the LDAP configuratino file are applied to your Big Data Extensions environment. Clusters you create will use the new LDAP settings.

What to do next

You can create clusters and assign user management roles using the users and user groups defined by your LDAP or Active Directory service. See “Create a Cluster With LDAP User Authentication Using the

Serengeti Command-Line Interface,” on page 41.

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Creating Hadoop and HBase Clusters 12

Big Data Extensions you can create and deploy Hadoop and HBase clusters. A big data cluster is a type of computational cluster designed for storing and analyzing large amounts of unstructured data in a distributed computing environment.

Restrictions

When you create an HBase only cluster, you must use the default application manager because the

other application managers do not support HBase only clusters.

You cannot rename a cluster that was created with Cloudera Manager or Ambari application manager.

Temporarily powering off hosts will cause Big Data clusters to fail during cluster creation.

When creating Big Data clusters, Big Data Extensions calculates virtual machine placement according to available resources, Hadoop best practices, and user defined placement policies prior to creating the virtual machines. When performing placement calculations, if some hosts are powered off or set to stand-by, either manually, or automatically by VMware Distributed Power Management (VMware DPM), those hosts will not be considered as available resources when Big Data Extensions calculates virtual machine placement for use with a Big Data cluster.

If a host is powered off or set to stand-by after Big Data Extensions calculates virtual machine placement, but before it creates the virtual machines, the cluster fails to create until you power on those hosts. The following workarounds can help you both prevent and recover from this issue.

Disable VMware DPM on those vSphere clusters where you deploy and run Big Data Extensions.

Put hosts in maintenance mode before you power them off.

If a Big Data cluster fails to create due to its assigned hosts being temporarily unavailable, resume

the cluster creation after you power-on the hosts.

Requirements

The resource requirements are different for clusters created with the Serengeti Command-Line Interface and the Big Data Extensions plug-in for the vSphere Web Client because the clusters use different default templates. The default clusters created by using the Serengeti CLI are targeted for Project Serengeti users and proof-of-concept applications, and are smaller than the Big Data Extensions plug-in templates, which are targeted for larger deployments for commercial use.

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Some deployment configurations require more resources than other configurations. For example, if you create a Greenplum HD 1.2 cluster, you cannot use the small size virtual machine. If you create a default MapR or Greenplum HD cluster by using the Serengeti CLI, at least 550 GB of storage and 55 GB of memory are recommended. For other Hadoop distributions, at least 350 GB of storage and 35 GB of memory are recommended.

CAUTION When you create a cluster with Big Data Extensions, Big Data Extensions disables the virtual machine automatic migration on the cluster. Although this prevents vSphere from automatically migrating the virtual machines, it does not prevent you from inadvertently migrating cluster nodes to other hosts by using the vCenter Server user interface. Do not use the vCenter Server user interface to migrate clusters. Performing such management functions outside of the Big Data Extensions environment can make it impossible for you to perform some Big Data Extensions operations, such as disk failure recovery.

Passwords must be from 8 to 20 characters, use only visible lowerASCII characters (no spaces), and must contain at least one uppercase alphabetic character (A - Z), at least one lowercase alphabetic character (a - z), at least one digit (0 - 9), and at least one of the following special characters: _, @, #, $, %, ^, &, *

This chapter includes the following topics:

“About Hadoop and HBase Cluster Deployment Types,” on page 47

“Default Hadoop Cluster Configuration for Serengeti,” on page 47

“Default HBase Cluster Configuration for Serengeti,” on page 48

“About Cluster Topology,” on page 48

“About HBase Clusters,” on page 51

“About MapReduce Clusters,” on page 58

“About Data Compute Clusters,” on page 61

“About Customized Clusters,” on page 71

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Chapter 12 Creating Hadoop and HBase Clusters

About Hadoop and HBase Cluster Deployment Types

With Big Data Extensions, you can create and use several types of big data clusters.

Basic Hadoop Cluster

HBase Cluster

Data and Compute Separation Cluster

Compute Only Cluster

Compute Workers Only Cluster

HBase Only Cluster

Simple Hadoop deployment for proof of concept projects and other smallscale data processing tasks. The Basic Hadoop cluster contains HDFS and the MapReduce framework. The MapReduce framework processes problems in parallel across huge datasets in the HDFS.

Runs on top of HDFS and provides a fault-tolerant way of storing large quantities of sparse data.

Separates the data and compute nodes, or clusters that contain compute nodes only. In this type of cluster, the data node and compute node are not on the same virtual machine.

You can create a cluster that contain only compute nodes, for example Jobtracker, Tasktracker, ResourceManager and NodeManager nodes, but not Namenode and Datanodes. A compute only cluster is used to run MapReduce jobs on an external HDFS cluster.

Contains only compute worker nodes, for example, Tasktracker and NodeManager nodes, but not Namenodes and Datanodes. A compute workers only cluster is used to add more compute worker nodes to an existing Hadoop cluster.

Contains HBase Master, HBase RegionServer, and Zookeeper nodes, but not Namenodes or Datanodes. Multiple HBase only clusters can use the same external HDFS cluster.

Customized Cluster

Uses a cluster specification file to create clusters using the same configuration as your previously created clusters. You can edit the cluster specification file to customize the cluster configuration.

Default Hadoop Cluster Configuration for Serengeti

For basic Hadoop deployments, such as proof of concept projects, you can use the default Hadoop cluster configuration for Serengeti for clusters that are created with the CLI.

The resulting cluster deployment consists of the following nodes and virtual machines:

One master node virtual machine with NameNode and JobTracker services.

Three worker node virtual machines, each with DataNode and TaskTracker services.

One client node virtual machine containing the Hadoop client environment: the Hadoop client shell,

Pig, and Hive.

Hadoop Distributions Supporting MapReduce v1 and MapReduce v2 (YARN)

If you use either Cloudera CDH4 or CDH5 Hadoop distributions, which support both MapReduce v1 and MapReduce v2 (YARN), the default Hadoop cluster configurations are different. The default hadoop cluster configuration for CDH4 is a MapReduce v1 cluster. The default hadoop cluster configuration for CDH5 is a MapReduce v2 cluster. All other distributions support either MapReduce v1 or MapReduce v2 (YARN), but not both.

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Default HBase Cluster Configuration for Serengeti

HBase is an open source distributed columnar database that uses MapReduce and HDFS to manage data. You can use HBase to build big table applications.

To run HBase MapReduce jobs, configure the HBase cluster to include JobTracker nodes or TaskTracker nodes. When you create an HBase cluster with the CLI, according to the default Serengeti HBase template, the resulting cluster consists of the following nodes:

One master node, which runs the NameNode and HBaseMaster services.

Three zookeeper nodes, each running the ZooKeeper service.

Three data nodes, each running the DataNode and HBase Regionserver services.

One client node, from which you can run Hadoop or HBase jobs.

The default HBase cluster deployed by Serengeti does not contain Hadoop JobTracker or Hadoop TaskTracker daemons. To run an HBase MapReduce job, deploy a customized, nondefault HBase cluster.

About Cluster Topology

You can improve workload balance across your cluster nodes, and improve performance and throughput, by specifying how Hadoop virtual machines are placed using topology awareness. For example, you can have separate data and compute nodes, and improve performance and throughput by placing the nodes on the same set of physical hosts.

To get maximum performance out of your big data cluster, configure your cluster so that it has awareness of the topology of your environment's host and network information. Hadoop performs better when it uses within-rack transfers, where more bandwidth is available, to off-rack transfers when assigning MapReduce tasks to nodes. HDFS can place replicas more intelligently to trade off performance and resilience. For example, if you have separate data and compute nodes, you can improve performance and throughput by placing the nodes on the same set of physical hosts.

CAUTION When you create a cluster with Big Data Extensions, Big Data Extensions disables the virtual machine automatic migration of the cluster. Although this prevents vSphere from migrating the virtual machines, it does not prevent you from inadvertently migrating cluster nodes to other hosts by using the vCenter Server user interface. Do not use the vCenter Server user interface to migrate clusters. Performing such management functions outside of the Big Data Extensions environment might break the placement policy of the cluster, such as the number of instances per host and the group associations. Even if you do not specify a placement policy, using vCenter Server to migrate clusters can break the default ROUNDROBIN placement policy constraints.

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You can specify the following topology awareness configurations.

Hadoop Virtualization Extensions (HVE)

Enhanced cluster reliability and performance provided by refined Hadoop replica placement, task scheduling, and balancer policies. Hadoop clusters implemented on a virtualized infrastructure have full awareness of the topology on which they are running when using HVE.

To use HVE, your Hadoop distribution must support HVE and you must create and upload a topology rack-hosts mapping file.

Chapter 12 Creating Hadoop and HBase Clusters

RACK_AS_RACK

Standard topology for Apache Hadoop distributions. Only rack and host information are exposed to Hadoop. To use RACK_AS_RACK, create and upload a server topology file.

HOST_AS_RACK

Simplified topology for Apache Hadoop distributions. To avoid placing all HDFS data block replicas on the same physical host, each physical host is treated as a rack. Because data block replicas are never placed on a rack, this avoids the worst case scenario of a single host failure causing the complete loss of any data block.

Use HOST_AS_RACK if your cluster uses a single rack, or if you do not have rack information with which to decide about topology configuration options.

None

No topology is specified.

Topology Rack-Hosts Mapping File

Rack-hosts mapping files are plain text files that associate logical racks with physical hosts. These files are required to create clusters with HVE or RACK_AS_RACK topology.

The format for every line in a topology rack-hosts mapping file is:

rackname: hostname1, hostname2 ...

For example, to assign physical hosts a.b.foo.com and a.c.foo.com to rack1, and physical host c.a.foo.com to rack2, include the following lines in your topology rack-hosts mapping file.

rack1: a.b.foo.com, a.c.foo.com rack2: c.a.foo.com

Topology Placement Policy Definition Files

The placementPolicies field in the cluster specification file controls how nodes are placed in the cluster.

If you specify values for both instancePerHost and groupRacks, there must be a sufficient number of available hosts. To display the rack hosts information, use the topology list command.

The code shows an example placementPolicies field in a cluster specification file.

{ "nodeGroups":[ … { "name": "group_name", … "placementPolicies": { "instancePerHost": 2, "groupRacks": { "type": "ROUNDROBIN", "racks": ["rack1", "rack2", "rack3"] },

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"groupAssociations": [{ "reference": "another_group_name", "type": "STRICT" // or "WEAK" }] } }, … }

Table 12‑1. placementPolicies Object Definition

JSON field Type Description

instancePerHost Optional Number of virtual machine nodes to

groupRacks Optional Method of distributing virtual machine

groupAssociations Optional One or more target node groups with

place for each physical ESXi host. This constraint is aimed at balancing the workload.

nodes among the cluster’s physical racks. Specify the following JSON strings:

type. Specify ROUNDROBIN, which selects candidates fairly and without priority.

racks. Which racks in the topology map to use.

which this node group associates. Specify the following JSON strings:

reference. Target node group name

type:

STRICT. Place the node group on

the target group’s set or subset of ESXi hosts. If STRICT placement is not possible, the operation fails.

WEAK. Attempt to place the node

group on the target group’s set or subset of ESXi hosts, but if that is not possible, use an extra ESXi host.

Create a Cluster with Topology Awareness with the Serengeti Command-Line Interface

To achieve a balanced workload or to improve performance and throughput, you can control how Hadoop virtual machines are placed by adding topology awareness to the Hadoop clusters. For example, you can have separate data and compute nodes, and improve performance and throughput by placing the nodes on the same set of physical hosts.

Prerequisites

Deploy the Serengeti vApp.

Ensure that you have adequate resources allocated to run the Hadoop cluster.

To use any Hadoop distribution other than the default distribution, add one or more Hadoop

distributions. See the VMware vSphere Big Data Extensions Administrator's and User's Guide.

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Procedure

1 Access the Serengeti CLI.

2 (Optional) Run the topology list command to view the list of available topologies.

topology list

3 (Optional) If you want the cluster to use HVE or RACK_AS_RACK toplogies, create a topology rack-

hosts mapping file and upload the file to the Serengeti Management Server.

topology upload --fileName name_of_rack_hosts_mapping_file

4 Run the cluster create command to create the cluster.

cluster create --name cluster-name ... --topology {HVE|RACK_AS_RACK|HOST_AS_RACK}

NOTE To create an Apache Bigtop, Cloudera CDH4 and CDH5, Hortonworks HDP 2.x, or Pivotal PHD

1.1 or later cluster, you must configure a valid DNS and FQDN for the cluster's HDFS and MapReduce network traffic. If the DNS server cannot provide valid forward and reverse FQDN/IP resolution, the cluster creation process might fail or the cluster is created but does not function.

This example creates an HVE topology.

cluster create --name cluster-name --topology HVE --distro name_of_HVE-supported_distro

5 View the allocated nodes on each rack.

cluster list --name cluster-name –-detail

About HBase Clusters

HBase runs on top of HDFS and provides a fault-tolerant way of storing large quantities of sparse data.

Create a Default HBase Cluster by Using the Serengeti Command-Line Interface

You can use the Serengeti CLI to deploy HBase clusters on HDFS.

This task creates a default HBase cluster which does not contain the MapReduce framework. To run HBase MapReduce jobs, add Jobtracker and TaskTracker or ResourceManager and NodeManager nodes to the default HBase cluster sample specification file /opt/serengeti/samples/default_hbase_cluster.json, then create a cluster using this specification file.

Prerequisites

Deploy the Serengeti vApp.

Ensure that you have adequate resources allocated to run the Hadoop cluster.

To use any Hadoop distribution other than the default distribution, add one or more Hadoop

distributions. See the VMware vSphere Big Data Extensions Administrator's and User's Guide.

Procedure

1 Access the Serengeti CLI.

2 Run the cluster create command, and specify the value of the --type parameter as hbase.

cluster create --name cluster_name --type hbase

What to do next

After you deploy the cluster, you can access an HBase database by using several methods. See the VMware vSphere Big Data Extensions Administrator's and User's Guide.

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Create an HBase Only Cluster in Big Data Extensions

With Big Data Extensions, you can create an HBase only cluster, which contain only HBase Master, HBase RegionServer, and Zookeeper nodes, but not Namenodes and Datanodes. The advantage of having an HBase only cluster is that multiple HBase clusters can use the same external HDFS.

Procedure

1 Prerequisites for Creating an HBase Only Cluster on page 52

Before you can create an HBase only cluster, you must verify that your system meets all of the prerequisites.

2 Prepare the EMC Isilon OneFS as the External HDFS Cluster on page 52

If you use EMC Isilon OneFS as the external HDFS cluster to the HBase only cluster, you must create and configure users and user groups, and prepare your Isilon OneFS environment.

3 Create an HBase Only Cluster by Using the Serengeti Command-Line Interface on page 53

You can use the Serengeti CLI to create an HBase only cluster.

Prerequisites for Creating an HBase Only Cluster

Before you can create an HBase only cluster, you must verify that your system meets all of the prerequisites.

Prerequisites

Verify that you started the Serengeti vApp.

Verify that you have more than one distribution if you want to use a distribution other than the default

distribution.

Verify that you have an existing HDFS cluster to use as the external HDFS cluster.

To avoid conflicts between the HBase only cluster and the external HDFS cluster, the clusters should use the same Hadoop distribution and version.

If the external HDFS cluster was not created using Big Data Extensions, verify that the HDFS

directory /hadoop/hbase, the group hadoop, and the following users exist in the external HDFS cluster:

hdfs

hbase

serengeti

If you use the EMC Isilon OneFS as the external HDFS cluster, verify that your Isilon environment is

prepared.

For information about how to prepare your environment, see “Prepare the EMC Isilon OneFS as the

External HDFS Cluster,” on page 52.

Prepare the EMC Isilon OneFS as the External HDFS Cluster

If you use EMC Isilon OneFS as the external HDFS cluster to the HBase only cluster, you must create and configure users and user groups, and prepare your Isilon OneFS environment.

Procedure

1 Log in to one of the Isilon HDFS nodes as user root.

2 Create the users.

hdfs

hbase

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Chapter 12 Creating Hadoop and HBase Clusters

serengeti

mapred

The yarn and mapred users should have write, read, and execute permissions to the entire exported HDFS directory.

3 Create the user group hadoop.

4 Create the directory tmp under the root HDFS directory.

5 Set the owner as hdfs:hadoop with the read and write permissions set as 777.

6 Create the directory hadoop under the root HDFS directory.

7 Set the owner as hdfs:hadoop with the read and write permissions set as 775.

8 Create the directory hbase under the directory hadoop.

9 Set the owner as hbase:hadoop with the read and write permissions set as 775.

10 Set the owner of the root HDFS directory as hdfs:hadoop.

Example: Configuring the EMC Isilon OneFS Environment

isi auth users create --name="hdfs" isi auth users create --name="hbase" isi auth users create --name="serengeti" isi auth groups create --name="hadoop" pw useradd mapred -G wheel pw useradd yarn -G wheel chown hdfs:hadoop /ifs mkdir /ifs/tmp chmod 777 /ifs/tmp chown hdfs:hadoop /ifs/tmp mkdir -p /ifs/hadoop/hbase chmod -R 775 /ifs/hadoop chown hdfs:hadoop /ifs/hadoop chown hbase:hadoop /ifs/hadoop/hbase

What to do next

You are now ready to create the HBase only cluster with the EMC Isilon OneFS as the external cluster.

Create an HBase Only Cluster by Using the Serengeti Command-Line Interface

You can use the Serengeti CLI to create an HBase only cluster.

You must use the default application manager because the other application managers do not support HBase only clusters.

Procedure

1 To define the characteristics of the new cluster, make a copy of the following cluster specification

file: /opt/serengeti/samples/hbase_only_cluster.json

2 Replace hdfs://hostname-of-namenode:8020 in the specification file with the namenode uniform

resource identifier (URI) of the external HDFS cluster.

3 Access the Serengeti CLI.

4 Run the cluster create command.

cluster create --name clustername --distro distroname

--specfile specfile_location

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The /opt/serengeti/samples/hbase_only_cluster.json file is a sample specification file for HBase only clusters. It contains the zookeeper, hbase_master, and hbase_regionserver roles, but not the hadoop_namenode/hadoop_datanode role.

5 To verify that the cluster was created, run the cluster list command.

cluster list --name name

After the cluster is created, the system returns Cluster clustername created.

Create an HBase Cluster with vSphere HA Protection with the Serengeti Command-Line Interface

You can create HBase clusters with separated Hadoop NameNode and HBase Master roles. You can configure vSphere HA protection for the Master roles.

Prerequisites

Deploy the Serengeti vApp.

Ensure that you have adequate resources allocated to run the Hadoop cluster.

To use any Hadoop distribution other than the default distribution, add one or more Hadoop

distributions. See the VMware vSphere Big Data Extensions Administrator's and User's Guide.

Procedure

1 Create a cluster specification file to define the characteristics of the cluster, including the node group

roles and vSphere HA protection.

In this example, the cluster has JobTracker and TaskTracker nodes, which let you run HBase MapReduce jobs. The Hadoop NameNode and HBase Master roles are separated, and both are protected by vSphere HA.

{ "nodeGroups" : [ { "name" : "zookeeper", "roles" : [ "zookeeper" ], "instanceNum" : 3, "instanceType" : "SMALL", "storage" : { "type" : "shared", "sizeGB" : 20 }, "cpuNum" : 1, "memCapacityMB" : 3748, "haFlag" : "on", "configuration" : { } }, { "name" : "hadoopmaster", "roles" : [ "hadoop_namenode", "hadoop_jobtracker" ], "instanceNum" : 1,

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"instanceType" : "MEDIUM", "storage" : { "type" : "shared", "sizeGB" : 50 }, "cpuNum" : 2, "memCapacityMB" : 7500, "haFlag" : "on", "configuration" : { } }, { "name" : "hbasemaster", "roles" : [ "hbase_master" ], "instanceNum" : 1, "instanceType" : "MEDIUM", "storage" : { "type" : "shared", "sizeGB" : 50 }, "cpuNum" : 2, "memCapacityMB" : 7500, "haFlag" : "on", "configuration" : { } },

Chapter 12 Creating Hadoop and HBase Clusters

{ "name" : "worker", "roles" : [ "hadoop_datanode", "hadoop_tasktracker", "hbase_regionserver" ], "instanceNum" : 3, "instanceType" : "SMALL", "storage" : { "type" : "local", "sizeGB" : 50 }, "cpuNum" : 1, "memCapacityMB" : 3748, "haFlag" : "off", "configuration" : { } }, { "name" : "client", "roles" : [ "hadoop_client", "hbase_client" ], "instanceNum" : 1,

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"instanceType" : "SMALL", "storage" : { "type" : "shared", "sizeGB" : 50 }, "cpuNum" : 1, "memCapacityMB" : 3748, "haFlag" : "off", "configuration" : { } } ], // we suggest running convert-hadoop-conf.rb to generate "configuration" section and paste the output here "configuration" : { "hadoop": { "core-site.xml": { // check for all settings at http://hadoop.apache.org/common/docs/stable/coredefault.html // note: any value (int, float, boolean, string) must be enclosed in double quotes and here is a sample: // "io.file.buffer.size": "4096" }, "hdfs-site.xml": { // check for all settings at http://hadoop.apache.org/common/docs/stable/hdfsdefault.html }, "mapred-site.xml": { // check for all settings at http://hadoop.apache.org/common/docs/stable/mapreddefault.html }, "hadoop-env.sh": { // "HADOOP_HEAPSIZE": "", // "HADOOP_NAMENODE_OPTS": "", // "HADOOP_DATANODE_OPTS": "", // "HADOOP_SECONDARYNAMENODE_OPTS": "", // "HADOOP_JOBTRACKER_OPTS": "", // "HADOOP_TASKTRACKER_OPTS": "", // "HADOOP_CLASSPATH": "", // "JAVA_HOME": "", // "PATH": "" }, "log4j.properties": { // "hadoop.root.logger": "DEBUG,DRFA", // "hadoop.security.logger": "DEBUG,DRFA" }, "fair-scheduler.xml": { // check for all settings at http://hadoop.apache.org/docs/stable/fair_scheduler.html // "text": "the full content of fair-scheduler.xml in one line" }, "capacity-scheduler.xml": { // check for all settings at http://hadoop.apache.org/docs/stable/capacity_scheduler.html },

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Chapter 12 Creating Hadoop and HBase Clusters

"mapred-queue-acls.xml": { // check for all settings at http://hadoop.apache.org/docs/stable/cluster_setup.html#Configuring+the+Hadoop+Daemons // "mapred.queue.queue-name.acl-submit-job": "", // "mapred.queue.queue-name.acl-administer-jobs", "" } }, "hbase": { "hbase-site.xml": { // check for all settings at http://hbase.apache.org/configuration.html#hbase.site }, "hbase-env.sh": { // "JAVA_HOME": "", // "PATH": "", // "HBASE_CLASSPATH": "", // "HBASE_HEAPSIZE": "", // "HBASE_OPTS": "", // "HBASE_USE_GC_LOGFILE": "", // "HBASE_JMX_BASE": "", // "HBASE_MASTER_OPTS": "", // "HBASE_REGIONSERVER_OPTS": "", // "HBASE_THRIFT_OPTS": "", // "HBASE_ZOOKEEPER_OPTS": "", // "HBASE_REGIONSERVERS": "", // "HBASE_SSH_OPTS": "", // "HBASE_NICENESS": "", // "HBASE_SLAVE_SLEEP": "" }, "log4j.properties": { // "hbase.root.logger": "DEBUG,DRFA" } }, "zookeeper": { "java.env": { // "JVMFLAGS": "-Xmx2g" }, "log4j.properties": { // "zookeeper.root.logger": "DEBUG,DRFA" } } } }

2 Access the Serengeti CLI.

3 Run the cluster create command, and specify the cluster specification file.

cluster create --name cluster_name --specFile full_path/spec_filename

Create an HBase Only Cluster with External Namenode HA HDFS Cluster

You can create an HBase only cluster with two namenodes in an active-passive HA configuration. The HA namenode provides a hot standby name node that, in the event of a failure, can perform the role of the active namenode with no downtime.

Worker only clusters are not supported on Ambari and Cloudera Manager application managers.

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MapReduce v1 worker only clusters and HBase only clusters created using the MapR distribution are

not supported.

Prerequisites

Deploy the Serengeti vApp.

Ensure that you have adequate resources allocated to run the Hadoop cluster.

To use any Hadoop distribution other than the default distribution, add one or more Hadoop

distributions. See the VMware vSphere Big Data Extensions Administrator's and User's Guide.

Procedure

1 To define the characteristics of the new cluster, make a copy of the following cluster specification

file: /opt/serengeti/samples/hbase_only_cluster.json

2 Replace hdfs://hostname-of-namenode:8020 in this spec file with the namenode uniform resource identifier

(URI) of the external namenode HA HDFS cluster. The namenode URI is the value of the fs.defaultFS parameter in the core-site.xml of the external cluster.

3 Change the configuration section of the HBase only cluster specification file as shown in the following

example. All the values can be found in hdfs-site.xml of the external cluster.

"configuration" : { "hadoop": { "hdfs-site.xml": { "dfs.nameservices": "dataMaster", "dfs.ha.namenodes.dataMaster": "namenode0,namenode1", "dfs.client.failover.proxy.provider.dataMaster": "org.apache.hadoop.hdfs.server.namenode.ha.ConfiguredFailoverProxyProvider", "dfs.namenode.rpc-address.dataMaster.namenode0": "10.555.xx.xxx:xxx1", "dfs.namenode.http-address.dataMaster.namenode0": "10.555.xx.xxx:xxx2", "dfs.namenode.rpc-address.dataMaster.namenode1": "10.555.xx.xxx:xxx3", "dfs.namenode.http-address.dataMaster.namenode1": "10.555.xx.xxx:xxx4" } } }

About MapReduce Clusters

MapReduce is a framework for processing problems in parallel across huge data sets. The MapReduce framework distributes a number of operations on the data set to each node in the network.

Create a MapReduce v2 (YARN) Cluster by Using the Serengeti Command-Line Interface

You can create MapReduce v2 (YARN) clusters if you want to create a cluster that separates the resource management and processing components.

To create a MapReduce v2 (YARN) cluster, create a cluster specification file modeled after the /opt/serengeti/samples/default_hadoop_yarn_cluster.json file, and specify the --specFile parameter and your cluster specification file in the cluster create ... command.

Prerequisites

Deploy the Serengeti vApp.

Ensure that you have adequate resources allocated to run the Hadoop cluster.

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Chapter 12 Creating Hadoop and HBase Clusters

To use any Hadoop distribution other than the default distribution, add one or more Hadoop

distributions. See the VMware vSphere Big Data Extensions Administrator's and User's Guide.

Procedure

1 Access the Serengeti CLI.

2 Run the cluster create ... command.

This example creates a customized MapReduce v2 cluster using the CDH4 distribution according to the sample cluster specification file default_hadoop_yarn_cluster.json.

cluster create --name cluster_name --distro cdh4 --specFile /opt/serengeti/samples/default_hadoop_yarn_cluster.json

Create a MapReduce v1 Worker Only Cluster with External Namenode HA HDFS Cluster

You can create a MapReduce v1 worker only cluster with two namenodes in an active-passive HA configuration. The HA namenode provides a hot standby namenode that, in the event of a failure, can perform the role of the active namenode with no downtime.

The following restrictions apply to this task:

Worker only clusters are not supported on Ambari and Cloudera Manager application managers.

You cannot use MapR distribution to create MapReduce v1 worker only clusters and HBase only

clusters

Prerequisites

Start the Big Data Extensions vApp.

Ensure that you have adequate resources allocated to run the Hadoop cluster.

Ensure that you have an External Namenode HA HDFS cluster.

To use any Hadoop distribution other than the default distribution, add one or more Hadoop

distributions. See the VMware vSphere Big Data Extensions Administrator's and User's Guide.

Procedure

1 To define the characteristics of the new cluster, open the following cluster specification file to

modify: /opt/serengeti/samples/compute_workers_only_mr1.json

2 Replace hdfs://hostname-of-namenode:8020 in this spec file with the namenode uniform resource identifier

(URI) of the external namenode HA HDFS cluster. The namenode URI is the value of the fs.defaultFS parameter in the core-site.xml of the external cluster.

3 Replace the hostname-of-jobtracker in the specification file with the FQDN or IP address of the JobTracker

in the external cluster.

4 Change the configuration section of the MapReduce Worker only cluster specification file as shown in

the following example. All the values can be found in hdfs-site.xml of the external cluster.

{ "externalHDFS": "hdfs://dataMaster", "externalMapReduce": "xx.xxx.xxx.xxx:8021", "nodeGroups":[ { "name": "worker", "roles": [ "hadoop_tasktracker" ],

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"instanceNum": 3, "cpuNum": 2, "memCapacityMB": 7500, "storage": { "type": "LOCAL", "sizeGB": 20 } } ], "configuration" : { "hadoop": { "hdfs-site.xml": { "dfs.nameservices": "dataMaster", "dfs.ha.namenodes.dataMaster": "namenode0,namenode1", "dfs.client.failover.proxy.provider.dataMaster": "org.apache.hadoop.hdfs.server.namenode.ha.ConfiguredFailoverProxyProvider", "dfs.namenode.rpc-address.dataMaster.namenode0": "10.111.xx.xxx:xxx2", "dfs.namenode.http-address.dataMaster.namenode0": "10.111.xx.xxx:xxx3", "dfs.namenode.rpc-address.dataMaster.namenode1": "10.111.xx.xxx:xxx4", "dfs.namenode.http-address.dataMaster.namenode1": "10.111.xx.xxx:xxx5" } } } }

Create a MapReduce v2 Worker Only Cluster with External Namenode HA HDFS Cluster

You can create a MapReduce v2 (Yarn) worker only cluster with two namenodes in an active-passive HA configuration. The HA namenode provides a hot standby namenode that, in the event of a failure, can perform the role of the active namenode with no downtime.

The following restrictions apply to this task:

Worker only clusters are not supported on Ambari and Cloudera Manager application managers.

You cannot use a MapR distribution to deploy MapReduce v1 worker only clusters and HBase only

clusters.

Prerequisites

Start the Big Data Extensions vApp.

Ensure that you have an external Namenode HA HDFS cluster.

Ensure that you have adequate resources allocated to run the Hadoop cluster.

To use any Hadoop distribution other than the default distribution, add one or more Hadoop

distributions. See the VMware vSphere Big Data Extensions Administrator's and User's Guide.

Procedure

1 To define the characteristics of the new cluster, open the following cluster specification file to

modify: /opt/serengeti/samples/compute_workers_only_yarn.json

2 Replace hdfs://hostname-of-namenode:8020 in this spec file with the namenode uniform resource identifier

(URI) of the external namenode HA HDFS cluster. The namenode URI is the value of the fs.defaultFS parameter in the core-site.xml of the external cluster.

3 Replace the hostname-of-resourcemanager in the specification file with the FQDN or IP address of the

ResourceManager in the external cluster.

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Chapter 12 Creating Hadoop and HBase Clusters

4 Change the configuration section of the Yarn Worker only cluster specification file as shown in the

following example. All the values can be found in hdfs-site.xml of the external cluster.

{ "externalHDFS": "hdfs://dataMaster", "externalMapReduce": "xx.xxx.xxx.xxx:8021", "nodeGroups":[ { "name": "worker", "roles": [ "hadoop_nodemanager" ], "instanceNum": 3, "cpuNum": 2, "memCapacityMB": 7500, "storage": { "type": "LOCAL", "sizeGB": 20 } } ], "configuration" : { "hadoop": { "hdfs-site.xml": { "dfs.nameservices": "dataMaster", "dfs.ha.namenodes.dataMaster": "namenode0,namenode1", "dfs.client.failover.proxy.provider.dataMaster": "org.apache.hadoop.hdfs.server.namenode.ha.ConfiguredFailoverProxyProvider", "dfs.namenode.rpc-address.dataMaster.namenode0": "10.555.xx.xxx:xxx1", "dfs.namenode.http-address.dataMaster.namenode0": "10.555.xx.xxx:xxx2", "dfs.namenode.rpc-address.dataMaster.namenode1": "10.555.xx.xxx:xxx3", "dfs.namenode.http-address.dataMaster.namenode1": "10.555.xx.xxx:xxx4" } } } }

About Data Compute Clusters

You can separate the data and compute nodes in a Hadoop cluster, and you can control how nodes are placed on the vSphere ESXi hosts in your environment.

You can create a compute-only cluster to run MapReduce jobs. Compute-only clusters run only MapReduce services that read data from external HDFS clusters and that do not need to store data.

Ambari and Cloudera Manager application managers do not support data-compute separation and compute-only clusters.

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Create a Data-Compute Separated Cluster with Topology Awareness and Placement Constraints

You can create clusters with separate data and compute nodes, and define topology and placement policy constraints to distribute the nodes among the physical racks and the virtual machines.

Prerequisites

Start the Serengeti vApp.

Ensure that you have adequate resources allocated to run the Hadoop cluster.

To use any Hadoop distribution other than the default distribution, add one or more Hadoop

distributions. See the VMware vSphere Big Data Extensions Administrator's and User's Guide.

Create a rack-host mapping information file.

Upload the rack-host file to the Serengeti server with the topology upload command.

Procedure

1 Create a cluster specification file to define the characteristics of the cluster, including the node groups,

topology, and placement constraints.

NOTE To create an Apache Bigtop, Cloudera CDH4 and CDH5, Hortonworks HDP 2.x, or Pivotal PHD

In this example, the cluster has groupAssociations and instancePerHost constraints for the compute node group, and a groupRacks constraint for the data node group.

Four data nodes and eight compute nodes are placed on the same four ESXi hosts, which are fairly selected from rack1, rack2, and rack3. Each ESXi host has one data node and two compute nodes. As defined for the compute node group, compute nodes are placed only on ESXi hosts that have data nodes.

This cluster definition requires that you configure datastores and resource pools for at least four hosts, and that there is sufficient disk space for Serengeti to perform the necessary placements during deployment.

{ "nodeGroups":[ { "name": "master", "roles": [ "hadoop_namenode", "hadoop_jobtracker" ], "instanceNum": 1,

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"cpuNum": 2, "memCapacityMB": 7500, }, { "name": "data", "roles": [ "hadoop_datanode" ], "instanceNum": 4, "cpuNum": 1, "memCapacityMB": 3748, "storage": { "type": "LOCAL", "sizeGB": 50 }, "placementPolicies": { "instancePerHost": 1, "groupRacks": { "type": "ROUNDROBIN", "racks": ["rack1", "rack2", "rack3"] }, } }, { "name": "compute", "roles": [ "hadoop_tasktracker" ], "instanceNum": 8, "cpuNum": 2, "memCapacityMB": 7500, "storage": { "type": "LOCAL", "sizeGB": 20 }, "placementPolicies": { "instancePerHost": 2, "groupAssociations": [ { "reference": "data", "type": "STRICT" } } }, { "name": "client", "roles": [ "hadoop_client", "hive", "pig" ], "instanceNum": 1, "cpuNum": 1, "storage": { "type": "LOCAL",

Chapter 12 Creating Hadoop and HBase Clusters

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"sizeGB": 50 } } ], "configuration": { } }

2 Access the Serengeti CLI.

3 Run the cluster create command, and specify the cluster specification file.

cluster create --name cluster_name --specFile full_path/spec_filename

Create a Data-Compute Separated Cluster with No Node Placement Constraints

You can create a cluster with separate data and compute nodes without node placement constraints.

Prerequisites

Deploy the Serengeti vApp.

Ensure that you have adequate resources allocated to run the Hadoop cluster.

To use any Hadoop distribution other than the default distribution, add one or more Hadoop

distributions. See the VMware vSphere Big Data Extensions Administrator's and User's Guide.

Procedure

1 Create a cluster specification file to define the characteristics of the cluster.

NOTE To create an Apache Bigtop, Cloudera CDH4 and CDH5, Hortonworks HDP 2.x, or Pivotal PHD

In this example, the cluster has separate data and compute nodes, without node placement constraints. Four data nodes and eight compute nodes are created and put into individual virtual machines. The number of nodes is configured by the instanceNum attribute.

{ "nodeGroups":[ { "name": "master", "roles": [ "hadoop_namenode", "hadoop_jobtracker" ], "instanceNum": 1, "cpuNum": 2, "memCapacityMB": 7500, }, { "name": "data", "roles": [ "hadoop_datanode" ], "instanceNum": 4, "cpuNum": 1, "memCapacityMB": 3748,

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"storage": { "type": "LOCAL", "sizeGB": 50 } }, { "name": "compute", "roles": [ "hadoop_tasktracker" ], "instanceNum": 8, "cpuNum": 2, "memCapacityMB": 7500, "storage": { "type": "LOCAL", "sizeGB": 20 } }, { "name": "client", "roles": [ "hadoop_client", "hive", "pig" ], "instanceNum": 1, "cpuNum": 1, "storage": { "type": "LOCAL", "sizeGB": 50 } } ], "configuration": { } }

Chapter 12 Creating Hadoop and HBase Clusters

2 Access the Serengeti CLI.

3 Run the cluster create command and specify the cluster specification file.

cluster create --name cluster_name --specFile full_path/spec_filename

Create a Data-Compute Separated Cluster with Placement Policy Constraints

You can create a cluster with separate data and compute nodes, and define placement policy constraints to distribute the nodes among the virtual machines as you want.

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Prerequisites

Deploy the Serengeti vApp.

Ensure that you have adequate resources allocated to run the Hadoop cluster.

To use any Hadoop distribution other than the default distribution, add one or more Hadoop

distributions. See the VMware vSphere Big Data Extensions Administrator's and User's Guide.

Procedure

1 Create a cluster specification file to define characteristics of the cluster, including the node groups and

placement policy constraints.

NOTE To create an Apache Bigtop, Cloudera CDH4 and CDH5, Hortonworks HDP 2.x, or Pivotal PHD

In this example, the cluster has data-compute separated nodes, and each node group has a

placementPolicy constraint. After a successful provisioning, four data nodes and eight compute nodes

are created and put into individual virtual machines. With the instancePerHost=1 constraint, the four data nodes are placed on four ESXi hosts. The eight compute nodes are put onto four ESXi hosts: two nodes on each ESXi host.

This cluster specification requires that you configure datastores and resource pools for at least four hosts, and that there is sufficient disk space for Serengeti to perform the necessary placements during deployment.

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"name": "compute", "roles": [ "hadoop_tasktracker" ], "instanceNum": 8, "cpuNum": 2, "memCapacityMB": 7500, "storage": { "type": "LOCAL", "sizeGB": 20 }, "placementPolicies": { "instancePerHost": 2 } }, { "name": "client", "roles": [ "hadoop_client", "hive", "pig" ], "instanceNum": 1, "cpuNum": 1, "storage": { "type": "LOCAL", "sizeGB": 50 } } ], "configuration": { } }

Chapter 12 Creating Hadoop and HBase Clusters

2 Access the Serengeti CLI.

3 Run the cluster create command, and specify the cluster specification file.

cluster create --name cluster_name --specFile full_path/spec_filename

Create a Compute-Only Cluster with the Default Application Manager

You can create compute-only clusters to run MapReduce jobs on existing HDFS clusters, including storage solutions that serve as an external HDFS.

NOTE To create an Apache Bigtop, Cloudera CDH4 and CDH5, Hortonworks HDP 2.x, or Pivotal PHD 1.1 or later cluster, you must configure a valid DNS and FQDN for the cluster's HDFS and MapReduce network traffic. If the DNS server cannot provide valid forward and reverse FQDN/IP resolution, the cluster creation process might fail or the cluster is created but does not function.

Prerequisites

Deploy the Serengeti vApp.

Ensure that you have adequate resources allocated to run the Hadoop cluster.

To use any Hadoop distribution other than the default distribution, add one or more Hadoop

distributions. See the VMware vSphere Big Data Extensions Administrator's and User's Guide.

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Procedure

1 Create a cluster specification file that is modeled on the Serengeti compute_only_cluster.json sample

cluster specification file found in the Serengeti cli/samples directory.

2 Add the following content to a new cluster specification file.

In this example, the externalHDFS field points to an HDFS. Assign the hadoop_jobtracker role to the

master node group and the hadoop_tasktracker role to the worker node group.

The externalHDFS field conflicts with node groups that have hadoop_namenode and hadoop_datanode roles. This conflict might cause the cluster creation to fail or, if successfully created, the cluster might not work correctly. To avoid this problem, define only a single HDFS.

{ "externalHDFS": "hdfs://hostname-of-namenode:8020", "nodeGroups": [ { "name": "master", "roles": [ "hadoop_jobtracker" ], "instanceNum": 1, "cpuNum": 2, "memCapacityMB": 7500, }, { "name": "worker", "roles": [ "hadoop_tasktracker", ], "instanceNum": 4, "cpuNum": 2, "memCapacityMB": 7500, "storage": { "type": "LOCAL", "sizeGB": 20 }, }, { "name": "client", "roles": [ "hadoop_client", "hive", "pig" ], "instanceNum": 1, "cpuNum": 1, "storage": { "type": "LOCAL", "sizeGB": 50 }, } ], “configuration” : { } }

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Chapter 12 Creating Hadoop and HBase Clusters

3 Access the Serengeti CLI.

4 Run the cluster create command and include the cluster specification file parameter and associated

filename.

cluster create --name cluster_name --distro distro_name --specFile path/spec_file_name

Create a Compute-Only Cluster with the Cloudera Manager Application Manager

You can create compute-only clusters to run MapReduce jobs on existing HDFS clusters, including storage solutions that serve as an external HDFS.

You can use a Cloudera Manager application manager with any external HDFS.

If you use EMC Isilon OneFS as the external HDFS cluster to the HBase only cluster, you must create and configure users and user groups, and prepare your Isilon OneFS environment. See “Prepare the EMC Isilon

OneFS as the External HDFS Cluster,” on page 52

Prerequisites

Deploy the Serengeti vApp.

Ensure that you have adequate resources allocated to run the Hadoop cluster.

To use any Hadoop distribution other than the default distribution, add one or more Hadoop

distributions. See the VMware vSphere Big Data Extensions Administrator's and User's Guide.

Procedure

1 Create a cluster specification file that is modeled on the yarn_compute_only_cluster.json sample

cluster specification file found in the directory /opt/serengeti/samples/cloudera-manager/ on the Serengeti server.

2 Add the following code to your new cluster specification file.

In this cluster specification file, the default_fs_name field points to an HDFS Namenode URI and the

webhdfs_url field points to an HDFS web URL.

{ "nodeGroups": [ { "name": "master", "roles": [ "YARN_RESOURCE_MANAGER", "YARN_JOB_HISTORY" ], "instanceNum": 1, "cpuNum": 2, "memCapacityMB": 7500, "storage": { "type": "SHARED", "sizeGB": 50 }, "haFlag": "on", "configuration": { } }, { "name": "worker", "roles": [

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"YARN_NODE_MANAGER", "GATEWAY" ], "instanceNum": 3, "cpuNum": 2, "memCapacityMB": 7500, "storage": { "type": "LOCAL", "sizeGB": 50 }, "haFlag": "off", "configuration": { } } ], "configuration": { "ISILON": { // service level configurations // check for all settings by running "appmanager list --name <name> --configurations" "default_fs_name": "hdfs://FQDN:8020", "webhdfs_url": "hdfs://FQDN:8020/webhdfs/v1" }, "YARN": { // service level configurations }, "YARN_RESOURCE_MANAGER": { }, "YARN_NODE_MANAGER": { "yarn_nodemanager_local_dirs": "/yarn/nm" } } }

3 Access the Serengeti CLI.

4 Run the cluster create command and include the cluster specification file parameter and associated

filename.

cluster create --name computeOnlyCluster_name -- appManager appManager_name

--distro distro_name --specFile path/spec_file_name

Create a Compute Workers Only Cluster With Non-Namenode HA HDFS Cluster

If you already have a physical Hadoop cluster and want to do more CPU or memory intensive operations, you can increase the compute capacity by provisioning a worker only cluster. The worker only cluster is a part of the physical Hadoop cluster and can be scaled out elastically.

With the compute workers only clusters, you can "burst out to virtual." It is a temporary operation that involves borrowing resources when you need them and then returning the resources when you no longer need them. With "burst out to virtual," you spin up compute only workers nodes and add them to either an existing physical or virtual Hadoop cluster.

Restrictions

Worker only clusters are not supported on Ambari and Cloudera

Manager application managers.

These options are not supported on compute workers only clusters.

--appmanager appmanager_name

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Chapter 12 Creating Hadoop and HBase Clusters

--type cluster_type

--hdfsNetworkName hdfs_network_name

--mapredNetworkName mapred_network_name

Prerequisites

Start the Big Data Extensions vApp.

Ensure that you have an existing Hadoop cluster.

Verify that you have the IP addresses of the NameNode and ResourceManager node.

Ensure that you have adequate resources allocated to run the Hadoop cluster.

To use any Hadoop distribution other than the default distribution, add one or more Hadoop

distributions. See the VMware vSphere Big Data Extensions Administrator's and User's Guide.

Procedure

1 To define the characteristics of the new cluster, make a copy of the following cluster specification

file:/opt/serengeti/samples/compute_workers_only_mr1.json

2 Replace hdfs://hostname-of-namenode:8020 in the specification file with the namenode uniform resource

identifier (URI) of the external HDFS cluster.

3 Replace the hostname-of-jobtracker in the specification file with the FQDN or IP address of the JobTracker

in the external cluster.

4 Change the configuration section of the MapReduce Worker only cluster specification file. All the

values can be found in hdfs-site.xml of the external cluster.

About Customized Clusters

You can use an existing cluster specification file to create clusters by using the same configuration as your previously created clusters. You can also edit the cluster specification file to customize the cluster configuration.

Create a Default Serengeti Hadoop Cluster with the Serengeti Command-Line Interface

You can create as many clusters as you want in your Serengeti environment but your environment must meet all prerequisites.

Prerequisites

Deploy the Serengeti vApp.

Ensure that you have adequate resources allocated to run the Hadoop cluster.

To use any Hadoop distribution other than the default distribution, add one or more Hadoop

distributions. See the VMware vSphere Big Data Extensions Administrator's and User's Guide.

Procedure

1 Access the Serengeti CLI.

2 Deploy a default Serengeti Hadoop cluster on vSphere.

Deploy the Serengeti vApp.

Ensure that you have adequate resources allocated to run the Hadoop cluster.

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To use any Hadoop distribution other than the provided Apache Bigtop, add one or more Hadoop

distributions. See the VMware vSphere Big Data Extensions Administrator's and User's Guide.

cluster create --name cluster_name

The only valid characters for cluster names are alphanumeric and underscores. When you choose the cluster name, also consider the applicable vApp name. Together, the vApp and cluster names must be < 80 characters.

During the deployment process, real-time progress updates appear on the command-line.

What to do next

After the deployment finishes, you can run Hadoop commands and view the IP addresses of the Hadoop node virtual machines from the Serengeti CLI.

Create a Basic Cluster with the Serengeti Command-Line Interface

You can create a basic cluster in your Serengeti environment. A basic cluster is a group of virtual machines provisioned and managed by Serengeti. Serengeti helps you to plan and provision the virtual machines to your specifications and use the virtual machines to install Big Data applications.

The basic cluster does not install the Big Data application packages used when creating a cluster. Instead, you can install and manage Big Data applications with third party application management tools such as Ambari or Cloudera Manager within your Big Data Extensions environment, and integrate it with your Hadoop software. The basic cluster does not deploy a cluster. You must deploy software into the virtual machines using an external third party application management tool.

The Serengeti package includes an annotated sample cluster specification file that you can use as an example when you create your basic cluster specification file. In the Serengeti Management Server, the sample specification file is located at /opt/serengeti/samples/basic_cluster.json. You can modify the configuration values in the sample cluster specification file to meet your requirements. The only value you cannot change is the value assigned to the role for each node group, which must always be basic.

You can deploy a basic cluster with the Big Data Extension plug-in using a customized cluster specification file.

To deploy software within the basic cluster virtual machines, use the cluster list --detail command, or run serengeti-ssh.sh cluster_name to obtain the IP address of the virtual machine. You can then use the IP address with management applications such as Ambari or Cloudera Manager to provision the virtual machine with software of your choosing. You can configure the management application to use the user name serengeti, and the password you specified when creating the basic cluster within Big Data Extensions when the management tool needs a user name and password to connect to the virtual machines.

Prerequisites

Deploy the Serengeti vApp.

Ensure that you have adequate resources allocated to run the cluster, as well as the Big Data software

you intend to deploy.

Procedure

1 Create a specification file to define the basic cluster's characteristics.

You must use the basic role for each node group you define for the basic cluster.

{ "nodeGroups":[ { "name": "master", "roles": [

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"basic" ], "instanceNum": 1, "cpuNum": 2, "memCapacityMB": 3768, "storage": { "type": "LOCAL", "sizeGB": 250 }, "haFlag": "on" }, { "name": "worker", "roles": [ "basic" ], "instanceNum": 1, "cpuNum": 2, "memCapacityMB": 3768, "storage": { "type": "LOCAL", "sizeGB": 250 }, "haFlag": "off" } ] }

Chapter 12 Creating Hadoop and HBase Clusters

2 Access the Serengeti CLI.

3 Run the cluster create command, and specify the basic cluster specification file.

cluster create --name cluster_name --specFile /opt/serengeti/samples/basic_cluster.json -password

NOTE When creating a basic cluster, you do not need to specify a Hadoop distribution type using the

--distro option. The reason for this is that there is no Hadoop distribution being installed within the

basic cluster to be managed by Serengeti.

Create a Cluster with an Application Manager by Using the Serengeti Command-Line Interface

You can use the Serengeti CLI to add a cluster with an application manager other than the default application manager. Then you can manage your cluster with the new application manager.

NOTE If you want to create a local yum repository, you must create the repository before you create the cluster.

Prerequisites

Connect to an application manager.

Ensure that you have adequate resources allocated to run the cluster. For information about resource

requirements, see the documentation for your application manager.

Verify that you have more than one distribution if you want to use a distribution other than the default

distribution. See the VMware vSphere Big Data Extensions Administrator's and User's Guide.

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Procedure

1 Access the Serengeti CLI.

2 Run the cluster command.

cluster create --name cluster_name --appManager appmanager_name

--[localrepoURL local_repository_url]

If you do not use the appManager parameter, the default application manager is used.

Create a Compute Workers Only Cluster by Using the vSphere Web Client

If you already have a physical Hadoop cluster and want to do more CPU or memory intensive operations, you can increase the compute capacity by provisioning a workers only cluster. The workers only cluster is a part of the physical Hadoop cluster and can be scaled out elastically.

Worker only clusters are not supported on Ambari and Cloudera Manager application managers.

Prerequisites

Ensure that you have an existing Hadoop cluster.

Verify that you have the IP addresses of the NameNode and ResourceManager node.

Procedure

1 Click Create Big Data Cluster on the objects pane.

2 In the Create Big Data Cluster wizard, choose the same distribution as the Hadoop cluster.

3 Set the DataMaster URL HDFS:namenode ip or fqdn:8020.

4 Set the ComputeMaster URL nodeManager ip or fqdn.

5 Follow the steps in the wizard and add the other resources.

There will be three node managers in the cluster. The three new node managers are registered to the resource manager.

Create a Cluster with a Custom Administrator Password with the Serengeti Command-Line Interface

When you create a cluster, you can assign a custom administrator password to all the nodes in the cluster. Custom administrator passwords let you directly log in to the nodes instead of having to first log in to the Serengeti Management server.

Prerequisites

Deploy the Serengeti vApp.

Ensure that you have adequate resources allocated to run the Hadoop cluster.

To use any Hadoop distribution other than the default distribution, add one or more Hadoop

distributions. See the VMware vSphere Big Data Extensions Administrator's and User's Guide.

Procedure

1 Access the Serengeti CLI.

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2 Run the cluster create command and include the --password parameter.

cluster create --name cluster_name --password

3 Enter your custom password, and enter it again.

Your custom password is assigned to all the nodes in the cluster.

Create a Cluster with an Available Distribution with the Serengeti CommandLine Interface

You can choose which Hadoop distribution to use when you deploy a cluster. If you do not specify a Hadoop distribution, the resulting cluster is created with the default distribution, Apache Bigtop.

Prerequisites

Deploy the Serengeti vApp.

Ensure that you have adequate resources allocated to run the Hadoop cluster.

To use any Hadoop distribution other than the default distribution, add one or more Hadoop

distributions. See the VMware vSphere Big Data Extensions Administrator's and User's Guide.

Procedure

1 Access the Serengeti CLI.

2 Run the cluster create command, and include the --distro parameter.

The --distro parameter’s value must match a distribution name displayed by the distro list command.

NOTE To create an Apache Bigtop, Cloudera CDH4 and CDH5, Hortonworks HDP 2.x, or Pivotal PHD

This example deploys a cluster with the Cloudera CDH distribution:

cluster create --name clusterName --distro cdh

This example creates a customized cluster named mycdh that uses the CDH5 Hadoop distribution, and is configured according to the /opt/serengeti/samples/default_cdh4_ha_and_federation_hadoop_cluster.json sample cluster specification file. In this sample file, nameservice0 and nameservice1 are federated. That is,

nameservice0 and nameservice1 are independent and do not require coordination with each other. The

NameNode nodes in the nameservice0 node group are HDFS2 HA enabled. In Serengeti, name node group names are used as service names for HDFS2.

cluster create --name mycdh --distro cdh5 --specFile /opt/serengeti/samples/default_cdh5_ha_hadoop_cluster.json

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Create a Cluster with Multiple Networks with the Serengeti Command-Line Interface

When you create a cluster, you can distribute the management, HDFS, and MapReduce traffic to separate networks. You might want to use separate networks to improve performance or to isolate traffic for security reasons.

For optimal performance, use the same network for HDFS and MapReduce traffic in Hadoop and Hadoop +HBase clusters. HBase clusters use the HDFS network for traffic related to the HBase Master and HBase RegionServer services.

IMPORTANT You cannot configure multiple networks for clusters that use the MapR Hadoop distribution.

Prerequisites

Deploy the Serengeti vApp.

Ensure that you have adequate resources allocated to run the Hadoop cluster.

To use any Hadoop distribution other than the default distribution, add one or more Hadoop

distributions. See the VMware vSphere Big Data Extensions Administrator's and User's Guide.

Procedure

1 Access the Serengeti CLI.

2 Run the cluster create command and include the --networkName, --hdfsNetworkName, and --

mapredNetworkName parameters.

cluster create --name cluster_name --networkName management_network [--hdfsNetworkName hdfs_network] [--mapredNetworkName mapred_network]

If you omit an optional network parameter, the traffic associated with that network parameter is routed on the management network that you specify by the --networkName parameter.

NOTE To create an Apache Bigtop, Cloudera CDH4 and CDH5, Hortonworks HDP 2.x, or Pivotal PHD

The cluster's management, HDFS, and MapReduce traffic is distributed among the specified networks.

Create a Cluster with Assigned Resources with the Serengeti Command-Line Interface

By default, when you use Serengeti to deploy a Hadoop cluster, the cluster might contain any or all available resources: vCenter Server resource pool for the virtual machine's CPU and memory, datastores for the virtual machine's storage, and a network. You can assign which resources the cluster uses by specifying specific resource pools, datastores, and/or a network when you create the Hadoop cluster.

Prerequisites

Deploy the Serengeti vApp.

Ensure that you have adequate resources allocated to run the Hadoop cluster.

To use any Hadoop distribution other than the default distribution, add one or more Hadoop

distributions. See the VMware vSphere Big Data Extensions Administrator's and User's Guide.

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Procedure

1 Access the Serengeti CLI.

2 Run the cluster create command, and specify any or all of the command’s resource parameters.

This example deploys a cluster named myHadoop on the myDS datastore, under the myRP resource pool, and uses the myNW network for virtual machine communications.

cluster create --name myHadoop --rpNames myRP --dsNames myDS --networkName myNW

Create a Cluster with Any Number of Master, Worker, and Client Nodes

You can create a Hadoop cluster with any number of master, worker, and client nodes.

Prerequisites

Deploy the Serengeti vApp.

Ensure that you have adequate resources allocated to run the Hadoop cluster.

To use any Hadoop distribution other than the default distribution, add one or more Hadoop

distributions. See the VMware vSphere Big Data Extensions Administrator's and User's Guide.

Procedure

1 Create a cluster specification file to define the cluster's characteristics, including the node groups.

NOTE To create an Apache Bigtop, Cloudera CDH4 and CDH5, Hortonworks HDP 2.x, or Pivotal PHD

In this example, the cluster has one master MEDIUM size virtual machine, five worker SMALL size virtual machines, and one client SMALL size virtual machine. The instanceNum attribute configures the number of virtual machines in a node.

{ "nodeGroups" : [ { "name": "master", "roles": [ "hadoop_namenode", "hadoop_jobtracker" ], "instanceNum": 1, "instanceType": "MEDIUM" }, { "name": "worker", "roles": [ "hadoop_datanode", "hadoop_tasktracker" ], "instanceNum": 5, "instanceType": "SMALL" }, { "name": "client", "roles": [

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"hadoop_client", "hive", "hive_server", "pig" ], "instanceNum": 1, "instanceType": "SMALL" } ] }

2 Access the Serengeti CLI.

3 Run the cluster create command, and specify the cluster specification file.

cluster create --name cluster_name --specFile directory_path/spec_filename

Create a Customized Hadoop or HBase Cluster with the Serengeti CommandLine Interface

You can create clusters that are customized for your requirements, including the number of nodes, virtual machine RAM and disk size, the number of CPUs, and so on.

The Serengeti package includes several annotated sample cluster specification files that you can use as models when you create your custom specification files.

In the Serengeti Management Server, the sample cluster specification files are

in /opt/serengeti/samples.

If you use the Serengeti Remote CLI client, the sample specification files are in the client directory.

Changing a node group role might cause the cluster creation process to fail. For example, workable clusters require a NameNode, so if there are no NameNode nodes after you change node group roles, you cannot create a cluster.

Prerequisites

Deploy the Serengeti vApp.

Ensure that you have adequate resources allocated to run the Hadoop cluster.

To use any Hadoop distribution other than the default distribution, add one or more Hadoop

distributions. See the VMware vSphere Big Data Extensions Administrator's and User's Guide.

Procedure

1 Create a cluster specification file to define the cluster's characteristics such as the node groups.

2 Access the Serengeti CLI.

3 Run the cluster create command, and specify the cluster specification file.

Use the full path to specify the file.

cluster create --name cluster_name --specFile full_path/spec_filename

NOTE To create an Apache Bigtop, Cloudera CDH4 and CDH5, Hortonworks HDP 2.x, or Pivotal PHD

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Managing Hadoop and HBase

Clusters 13

You can use the vSphere Web Client to start and stop your big data cluster and modify the cluster configuration. You can also manage a cluster using the Serengeti Command-Line Interface.

CAUTION Do not use vSphere management functions such as migrating cluster nodes to other hosts for clusters that you create with Big Data Extensions. Performing such management functions outside of the Big Data Extensions environment can make it impossible for you to perform some Big Data Extensions operations, such as disk failure recovery.

This chapter includes the following topics:

“Stop and Start a Cluster with the Serengeti Command-Line Interface,” on page 79

“Scale Out a Cluster with the Serengeti Command-Line Interface,” on page 80

“Scale CPU and RAM with the Serengeti Command-Line Interface,” on page 80

“Reconfigure a Cluster with the Serengeti Command-Line Interface,” on page 81

“About Resource Usage and Elastic Scaling,” on page 83

“Delete a Cluster by Using the Serengeti Command-Line Interface,” on page 88

“About vSphere High Availability and vSphere Fault Tolerance,” on page 88

“Reconfigure a Node Group with the Serengeti Command-Line Interface,” on page 88

“Recover from Disk Failure with the Serengeti Command-Line Interface Client,” on page 89

“Enter Maintenance Mode to Perform Backup and Restore with the Serengeti Command-Line Interface

Client,” on page 89

Stop and Start a Cluster with the Serengeti Command-Line Interface

You can stop a currently running cluster and start a stopped cluster from the Serengeti CLI. When you start or stop a cluster through Cloudera Manager or Ambari, only the services are started or stopped. However, when you start or stop a cluster through Big Data Extensions, not only the services, but also the virtual machines are started or stopped.

Prerequisites

Verify that the cluster is provisioned.

Verify that enough resources, especially CPU and memory, are available to start the virtual machines in

the Hadoop cluster.

Procedure

1 Access the Serengeti CLI.

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2 Run the cluster stop command.

cluster stop –-name name_of_cluster_to_stop

3 Run the cluster start command.

cluster start –-name name_of_cluster_to_start

Scale Out a Cluster with the Serengeti Command-Line Interface

You specify the number of nodes in the cluster when you create Hadoop and HBase clusters. You can later scale out the cluster by increasing the number of worker nodes and client nodes.

IMPORTANT Even if you changed the user password on the nodes of a cluster, the changed password is not used for the new nodes that are created when you scale out a cluster. If you set the initial administrator password for the cluster when you created the cluster, that initial administrator password is used for the new nodes. If you did not set the initial administrator password for the cluster when you created the cluster, new random passwords are used for the new nodes.

Prerequisites

Ensure that the cluster is started.

Procedure

1 Access the Serengeti CLI.

2 Run the cluster resize command.

For node_type, specify worker or client. For the instanceNum parameter’s num_nodes value, use any number that is larger than the current number of node_type instances.

cluster resize --name name_of_cluster_to_resize --nodeGroup node_type --instanceNum num_nodes

Scale CPU and RAM with the Serengeti Command-Line Interface

You can increase or decrease the compute capacity and RAM of a cluster to prevent memory resource contention of running jobs.

Serengeti lets you adjust compute and memory resources without increasing the workload on the master node. If increasing or decreasing the CPU of a cluster is unsuccessful for a node, which is commonly due to insufficient resources being available, the node is returned to its original CPU setting. If increasing or decreasing the RAM of a cluster is unsuccessful for a node, which is commonly due to insufficient resources, the swap disk retains its new setting anyway. The disk is not returned to its original memory setting.

Although all node types support CPU and RAM scaling, do not scale the master node of a cluster because Serengeti powers down the virtual machine during the scaling process.

The maximum CPU and RAM settings depend on the version of the virtual machine.

Table 13‑1. Maximum CPU and RAM Settings

Virtual Machine Version Maximum Number of CPUs Maximum RAM, in GB

7 8 255

8 32 1011

9 64 1011

10 64 1011

Prerequisites

Start the cluster if it is not running.

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Procedure

1 Access the Serengeti Command-Line Interface.

2 Run the cluster resize command to change the number of CPUs or the amount of RAM of a cluster.

Node types are either worker or client.

Specify one or both scaling parameters: --cpuNumPerNode or --memCapacityMbPerNode.

cluster resize --name cluster_name --nodeGroup node_type [--cpuNumPerNode vCPUs_per_node] [--memCapacityMbPerNode memory_per_node]

Reconfigure a Cluster with the Serengeti Command-Line Interface

You can reconfigure any big data cluster that you create with Big Data Extensions.

The cluster configuration is specified by attributes in Hadoop distribution XML configuration files such as:

core-site.xml, hdfs-site.xml, mapred-site.xml, hadoop-env.sh, yarn-env.sh, yarn-site.sh, and hadoopmetrics.properties.

NOTE Always use the cluster config command to change the parameters specified by the configuration files. If you manually modify these files, your changes will be erased if the virtual machine is rebooted, or you use the cluster config, cluster start, cluster stop, or cluster resize commands.

Procedure

1 Use the cluster export command to export the cluster specification file for the cluster that you want to

reconfigure.

cluster export --name cluster_name --specFile file_path/cluster_spec_file_name

Option Description

cluster_name

file_path

cluster_spec_file_name

Name of the cluster that you want to reconfigure.

The file system path to which to export the specification file.

The name with which to label the exported cluster specification file.

2 Edit the configuration information located near the end of the exported cluster specification file.

If you are modeling your configuration file on existing Hadoop XML configuration files, use the

convert-hadoop-conf.rb conversion tool to convert Hadoop XML configuration files to the required

JSON format.

… "configuration": { "hadoop": { "core-site.xml": { // check for all settings at http://hadoop.apache.org/common/docs/stable/coredefault.html // note: any value (int, float, boolean, string) must be enclosed in double quotes and here is a sample: // "io.file.buffer.size": "4096" }, "hdfs-site.xml": { // check for all settings at http://hadoop.apache.org/common/docs/stable/hdfsdefault.html }, "mapred-site.xml": { // check for all settings at http://hadoop.apache.org/common/docs/stable/mapred-

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default.html }, "hadoop-env.sh": { // "HADOOP_HEAPSIZE": "", // "HADOOP_NAMENODE_OPTS": "", // "HADOOP_DATANODE_OPTS": "", // "HADOOP_SECONDARYNAMENODE_OPTS": "", // "HADOOP_JOBTRACKER_OPTS": "", // "HADOOP_TASKTRACKER_OPTS": "", // "HADOOP_CLASSPATH": "", // "JAVA_HOME": "", // "PATH": "", }, "log4j.properties": { // "hadoop.root.logger": "DEBUG, DRFA ", // "hadoop.security.logger": "DEBUG, DRFA ", }, "fair-scheduler.xml": { // check for all settings at http://hadoop.apache.org/docs/stable/fair_scheduler.html // "text": "the full content of fair-scheduler.xml in one line" }, "capacity-scheduler.xml": { // check for all settings at http://hadoop.apache.org/docs/stable/capacity_scheduler.html } } } …

3 (Optional) If the JAR files of your Hadoop distribution are not in the $HADOOP_HOME/lib directory, add

the full path of the JAR file in $HADOOP_CLASSPATH to the cluster specification file.

This action lets the Hadoop daemons locate the distribution JAR files.

For example, the Cloudera CDH3 Hadoop Fair Scheduler JAR files are in /usr/lib/hadoop/contrib/fairscheduler/. Add the following to the cluster specification file to enable Hadoop to use the JAR files.

… "configuration": { "hadoop": { "hadoop-env.sh": { "HADOOP_CLASSPATH": "/usr/lib/hadoop/contrib/fairscheduler/*:$HADOOP_CLASSPATH" }, "mapred-site.xml": { "mapred.jobtracker.taskScheduler": "org.apache.hadoop.mapred.FairScheduler" … }, "fair-scheduler.xml": { … } } } …

4 Access the Serengeti CLI.

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5 Run the cluster config command to apply the new Hadoop configuration.

cluster config --name cluster_name --specFile file_path/cluster_spec_file_name

6 (Optional) Reset an existing configuration attribute to its default value.

a Remove the attribute from the configuration section of the cluster configuration file or comment

out the attribute using double back slashes (//).

b Re-run the cluster config command.

About Resource Usage and Elastic Scaling

Scaling lets you adjust the compute capacity of Hadoop data-compute separated clusters. When you enable elastic scaling for a Hadoop cluster, the Serengeti Management Server can stop and start compute nodes to match resource requirements to available resources. You can use manual scaling for more explicit cluster control.

Manual scaling is appropriate for static environments where capacity planning can predict resource availability for workloads. Elastic scaling is best suited for mixed workload environments where resource requirements and availability fluctuate.

When you select manual scaling, Big Data Extensions disables elastic scaling. You can configure the target number of compute nodes for manual scaling. If you do not configure the target number of compute nodes, Big Data Extensions sets the number of active compute nodes to the current number of active compute nodes. If nodes become unresponsive, they remain in the cluster and the cluster operates with fewer functional nodes. In contrast, when you enable elastic scaling, Big Data Extensions manages the number of active TaskTracker nodes according to the range that you specify, replacing unresponsive or faulty nodes with live, responsive nodes.

Chapter 13 Managing Hadoop and HBase Clusters

For both manual and elastic scaling, Big Data Extensions, not vCenter Server, controls the number of active nodes. However, vCenter Server applies the usual reservations, shares, and limits to the resource pool of a cluster according to the vSphere configuration of the cluster. vSphere DRS operates as usual, allocating resources between competing workloads, which in turn influences how Big Data Extensions dynamically adjusts the number of active nodes in competing Hadoop clusters while elastic scaling is in effect.

Big Data Extensions also lets you adjust the access priority for the datastores of cluster nodes by using the vSphere Storage I/O Control feature. Clusters configured for HIGH I/O shares receive higher priority access than clusters with NORMAL priority. Clusters configured for NORMAL I/O shares receive higher priority access than clusters with LOW priority. In general, higher priority provides better disk I/O performance.

Scaling Modes

To change between manual and elastic scaling, you change the scaling mode.

MANUAL. Big Data Extensions disables elastic scaling. When you change to manual scaling, you can

configure the target number of compute nodes. If you do not configure the target number of compute nodes, Big Data Extensions sets the number of active compute nodes to the current number of active compute nodes.

AUTO. Enables elastic scaling. Big Data Extensions manages the number of active compute nodes,

maintaining the number of compute nodes in the range from the configured minimum to the configured maximum number of compute nodes in the cluster. If the minimum number of compute nodes is undefined, the lower limit is 0. If the maximum number of compute nodes is undefined, the upper limit is the number of available compute nodes.

Elastic scaling operates on a per-host basis, at a node-level granularity. That is, the more compute nodes a Hadoop cluster has on a host, the finer the control that Big Data Extensions elasticity can exercise. The tradeoff is that the more compute nodes you have, the higher the overhead in terms of runtime resource cost, disk footprint, I/O requirements, and so on.

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When resources are overcommitted, elastic scaling reduces the number of powered on compute nodes. Conversely, if the cluster receives all the resources it requested from vSphere, and Big Data Extensions determines that the cluster can make use of additional capacity, elastic scaling powers on additional compute nodes.

Resources can become overcommitted for many reasons, such as:

The compute nodes have lower resource entitlements than a competing workload, according to

how vCenter Server applies the usual reservations, shares, and limits as configured for the cluster.

Physical resources are configured to be available, but another workload is consuming those

resources.

In elastic scaling, Big Data Extensions has two different behaviors for deciding how many active compute nodes to maintain. In both behaviors, Big Data Extensions replaces unresponsive or faulty nodes with live, responsive nodes.

Variable. The number of active, healthy TaskTracker compute nodes is maintained from the

configured minimum number of compute nodes to the configured maximum number of compute nodes. The number of active compute nodes varies as resource availability fluctuates.

Fixed. The number of active, healthy TaskTracker compute nodes is maintained at a fixed number

when the same value is configured for the minimum and maximum number of compute nodes.

Default Cluster Scaling Parameter Values

When you create a cluster, the scaling configuration of the cluster is as follows.

The scaling mode is MANUAL, for manual scaling.

The minimum number of compute nodes is -1. It appears as "Unset" in the Serengeti CLI displays. Big

Data Extensions elastic scaling treats a minComputeNodeNum value of -1 as if it were zero (0).

The maximum number of compute nodes is -1. It appears as "Unset" in the Serengeti CLI displays. Big

Data Extensions elastic scaling treats a maxComputeNodeNum value of -1 as if it were unlimited.

The target number of nodes is not applicable. Its value is -1. Big Data Extensions manual scaling

operations treat a targetComputeNodeNum value of -1 as if it were unspecified upon a change to manual scaling.

Interactions Between Scaling and Other Cluster Operations

Some cluster operations cannot be performed while Big Data Extensions is actively scaling a cluster.

If you try to perform the following operations while Big Data Extensions is scaling a cluster in MANUAL mode, Big Data Extensions warns you that in the current state of the cluster, the operation cannot be performed.

Concurrent attempt at manual scaling

Switch to AUTO mode while manual scaling operations are in progress

If a cluster is in AUTO mode for elastic scaling when you perform the following cluster operations on it, Big Data Extensions changes the scaling mode to MANUAL and changes the cluster to manual scaling. You can re-enable the AUTO mode for elastic scaling after the cluster operation finishes, except if you delete the cluster.

Delete the cluster

Repair the cluster

Stop the cluster

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If a cluster is in AUTO mode for elastic scaling when you perform the following cluster operations on it, Big Data Extensions temporarily switches the cluster to MANUAL mode. When the cluster operation finishes, Big Data Extensions returns the scaling mode to AUTO, which re-enables elastic scaling.

Resize the cluster

Reconfigure the cluster

If Big Data Extensions is scaling a cluster when you perform an operation that changes the scaling mode to MANUAL, your requested operation waits until the scaling finishes, and then the requested operation begins.

Enable Elastic Scaling for a Hadoop Cluster with the Serengeti Command-Line Interface

When you enable elastic scaling for a data-compute separated Hadoop cluster, Big Data Extensions optimizes cluster performance and utilization of TaskTracker compute nodes.

To enable elastic scaling, set the scaling mode of a data-compute separated Hadoop cluster to AUTO and configure the minimum and maximum number of compute nodes. If you do not configure the minimum or maximum number of compute nodes, the previous minimum or maximum setting, respectively, is retained.

To ensure that under contention, elastic scaling keeps a cluster operating with more than the initial default setting of zero compute nodes on a cluster, configure the minComputeNodeNum parameter value to a nonzero number. To limit the maximum number of compute nodes that can be used in a Hadoop cluster, configure the maxComputeNodeNum parameter value to less than the total available compute nodes.

Variable. The number of active, healthy TaskTracker compute nodes is maintained from the configured

minimum number of compute nodes to the configured maximum number of compute nodes. The number of active compute nodes varies as resource availability fluctuates.

Fixed. The number of active, healthy TaskTracker compute nodes is maintained at a fixed number when

the same value is configured for the minimum and maximum number of compute nodes.

Prerequisites

Understand how elastic scaling and resource usage work. See “About Resource Usage and Elastic

Scaling,” on page 83.

Verify that the cluster you want to optimize is data-compute separated. See “About Hadoop and HBase

Cluster Deployment Types,” on page 47.

Procedure

1 Access the Serengeti CLI.

2 Run the cluster setParam command, and set the --elasticityMode parameter value to AUTO.

cluster setParam --name cluster_name --elasticityMode AUTO [--minComputeNodeNum minNum] [--maxComputeNodeNum maxNum]

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Enable Manual Scaling for a Hadoop Cluster with the Serengeti Command-Line Interface

When you enable manual scaling for a cluster, Big Data Extensions disables elastic scaling. When you enable manual scaling, you can configure the target number of compute nodes. If you do not configure the target number of compute nodes, Big Data Extensions sets the number of active compute nodes to the current number of active compute nodes.

Procedure

1 Access the Serengeti CLI.

2 Run the cluster setParam command, and set the --elasticityMode parameter value to MANUAL.

cluster setParam --name cluster_name --elasticityMode MANUAL [--targetComputeNodeNum numTargetNodes]

Configure Scaling Parameters with the Serengeti Command-Line Interface

You can configure scaling parameters, such as the target number of nodes, with or without changing the scaling mode.

Procedure

1 Access the Serengeti CLI.

2 To display a cluster's scaling settings, run the cluster list command.

cluster list --detail --name cluster_name

3 To configure one or more scaling parameters, run the cluster setParam command.

The --name parameter is required, and you can include as few or as many of the other parameters as you want. You can repeatedly run the command to configure or reconfigure additional scaling parameters.

cluster setParam –-name cluster_name --elasticityMode mode

--targetComputeNodeNum numTargetNodes --minComputeNodeNum minNum --maxComputeNodeNum maxNum

--ioShares level

Paremeter Option Description

cluster_name

mode

numTargetNodes

minNum

maxNum

level

Name of the cluster. Specify this parameter every time you run the cluster setParam command.

MANUAL or AUTO.

Number of nodes. This parameter is applicable only for MANUAL scaling mode.

Lower limit of the range of active compute nodes to maintain in the cluster. This parameter is applicable only for AUTO scaling mode.

Upper limit of the range of active compute nodes to maintain in the cluster. This parameter is applicable only for AUTO scaling mode.

LOW, NORMAL, or HIGH.

4 To reset one or more scaling parameters to their default values, run the cluster resetParam command.

The --name parameter is required, and you can include as few or as many of the other parameters as you want. You can repeatedly run the command to reset additional scaling parameters.

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For data-compute separated nodes, you can reset all the scaling parameters to their defaults by using the --all parameter.

cluster resetParam -–name cluster_name [--all] [--elasticityMode] [--targetComputeNodeNum] [--minComputeNodeNum] [--maxComputeNodeNum] [--ioShares]

Parameter Description

cluster_name

--all

--elasticityMode

--targetComputeNodeNum

--minComputeNodeNum

--maxComputeNodeNum

--ioShares

Name of the cluster. Specify this parameter every time you run the cluster resetParam command.

Reset all scaling parameters to their defaults.

Sets the scaling mode to MANUAL.

Reset targetComputeNodeNum to -1. Big Data Extensions manual scaling operations treat a targetComputeNodeNum value of -1 as if it were unspecified upon a change to manual scaling.

Reset minComputeNodeNum to 0. It appears as "Unset" in the Serengeti CLI displays. Big Data Extensions elastic scaling treats a minComputeNodeNum value of -1 as if it were zero (0).

Reset maxComputeNodeNum to unlimited. It appears as "Unset" in the Serengeti CLI displays. Big Data Extensions elastic scaling treats a maxComputeNodeNum value of -1 as if it were unlimited.

Reset ioShares to NORMAL.

Schedule Fixed Elastic Scaling for a Hadoop Cluster

You can enable fixed, elastic scaling according to a preconfigured schedule. Scheduled fixed, elastic scaling provides more control than variable, elastic scaling while still improving efficiency, allowing explicit changes in the number of active compute nodes during periods of predictable usage.

For example, in an office with typical workday hours, there is likely a reduced load on a VMware View resource pool after the office staff goes home. You could configure scheduled fixed, elastic scaling to specify a greater number of compute nodes from 8 PM to 4 AM, when you know that the workload would otherwise be very light.

Prerequisites

From the Serengeti Command-Line Interface, enable the cluster for elastic scaling, and set the

minComputeNodeNum and MaxComputeNodeNum parameters to the same value: the number of active TaskTracker

nodes that you want during the period of scheduled fixed elasticity.

Procedure

1 Open a command shell, such as Bash or PuTTY, and log in to the Serengeti Management Server as user

serengeti.

2 Use any scheduling mechanism that you want to call

the /opt/serengeti/sbin/set_compute_node_num.sh script to set the number of active TaskTracker compute nodes that you want.

/opt/serengeti/sbin/set_compute_node_num.sh --name cluster_name

--computeNodeNum num_TT_to_maintain

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After the scheduling mechanism calls the set_compute_node_num.sh script, fixed, elastic scaling remains in effect with the configured number of active TaskTracker compute nodes until the next scheduling mechanism change or until a user changes the scaling mode or parameters in either the vSphere Web Client or the Serengeti Command-Line Interface.

This example shows how to use a crontab file on the Serengeti Management Server to schedule specific numbers of active TaskTracker compute nodes.

# cluster_A: use 20 active TaskTracker compute nodes from 11:00 to 16:00, and 30 compute nodes the rest of the day 00 11 * * * /opt/serengeti/sbin/set_compute_node_num.sh --name cluster_A

--computeNodeNum 20 >> $HOME/schedule_elasticity.log 2>&1 00 16 * * * /opt/serengeti/sbin/set_compute_node_num.sh --name cluster_A

--computeNodeNum 30 >> $HOME/schedule_elasticity.log 2>&1

# cluster_B: use 3 active TaskTracker compute nodes beginning at 10:00 every weekday 0 10 * * 1-5 /opt/serengeti/sbin/set_compute_node_num.sh --name cluster_B

--computeNodeNum 3 >> $HOME/schedule_elasticity.log 2>&1

# cluster_C: reset the number of active TaskTracker compute nodes every 6 hours to 15 0 */6 * * * /opt/serengeti/sbin/set_compute_node_num.sh --name cluster_B

--computeNodeNum 15 >> $HOME/schedule_elasticity.log 2>&1

Delete a Cluster by Using the Serengeti Command-Line Interface

You can delete a cluster that you no longer need, regardless of whether it is running. When a cluster is deleted, all its virtual machines and resource pools are destroyed.

Procedure

1 Access the Serengeti CLI.

2 Run the cluster delete command.

cluster delete --name cluster_name

About vSphere High Availability and vSphere Fault Tolerance

The Serengeti Management Server leverages vSphere HA to protect the Hadoop master node virtual machine, which can be monitored by vSphere.

When a Hadoop NameNode or JobTracker service stops unexpectedly, vSphere restarts the Hadoop virtual machine in another host, reducing unplanned downtime. If vsphere Fault Tolerance is configured and the master node virtual machine stops unexpectedly because of host failover or loss of network connectivity, the secondary node is used, without downtime.

Reconfigure a Node Group with the Serengeti Command-Line Interface

You can reconfigure node groups by modifying node group configuration data in the associated cluster specification file. When you configure a node group, its configuration overrides any cluster level configuration of the same name.

Procedure

1 Access the Serengeti CLI.

2 Run the cluster export command to export the cluster’s cluster specification file.

cluster export --name cluster_name --specFile path_name/spec_file_name

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3 In the specification file, modify the node group’s configuration section with the same content as a

cluster-level configuration.

4 Add the customized Hadoop configuration for the node group that you want to reconfigure.

5 Run the cluster config command to apply the new Hadoop configuration.

cluster config --name cluster_name --specFile path_name/spec_file_name

Recover from Disk Failure with the Serengeti Command-Line Interface Client

If there is a disk failure in a cluster, and the disk does not perform management roles such as NameNode, JobTracker, ResourceManager, HMaster, or ZooKeeper, you can recover by running the Serengeti cluster

fix command.

Big Data Extensions uses a large number of inexpensive disk drives for data storage (configured as JBOD). If several disks fail, the Hadoop data node might shutdown. Big Data Extensions enables you to recover from disk failures.

Serengeti supports recovery from swap and data disk failure on all supported Hadoop distributions. Disks are recovered and started in sequence to avoid the temporary loss of multiple nodes at once. A new disk matches the storage type and placement policies of the corresponding failed disk.

The MapR distribution does not support recovery from disk failure by using the cluster fix command.

IMPORTANT Even if you changed the user password on the nodes of the cluster, the changed password is not used for the new nodes that are created by the disk recovery operation. If you set the initial administrator password of the cluster when you created the cluster, that initial administrator password is used for the new nodes. If you did not set the initial administrator password of the cluster when you created the cluster, new random passwords are used for the new nodes.

Procedure

1 Access the Serengeti CLI.

2 Run the cluster fix command.

The nodeGroup parameter is optional.

cluster fix --name cluster_name --disk [--nodeGroup nodegroup_name]

Enter Maintenance Mode to Perform Backup and Restore with the Serengeti Command-Line Interface Client

Before performing backup and restore operations, or other maintenance tasks, you must place Big Data Extensions into maintenance mode.

Prerequisites

Deploy the Serengeti vApp.

Ensure that you have adequate resources allocated to run the Hadoop cluster.

To use any Hadoop distribution other than the default distribution, add one or more Hadoop

distributions. See the VMware vSphere Big Data Extensions Administrator's and User's Guide.

Procedure

1 Log into the Serengeti Management Server.

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2 Run the script /opt/serengeti/sbin/serengeti-maintenance.sh to place Big Data Extensions into

maintenance mode, or check maintenance status.

serengeti-maintenance.sh on | off | status

Option Description

off

status

Turns on maintenance mode. Upon entering maintenance mode, Big Data Extensions continues executing jobs that have already been started, but will not respond to any new requests.

Turn off maintenance mode, and returns Big Data Extensions to its normal operating state.

Displays the maintenance status of Big Data Extensions.

To place your Big Data Extensions deployment into maintenance mode, run the serengeti-

maintenance.sh script with the on option.

serengeti-maintenance.sh on

A status of safe means it is safe to backup or perform other maintenance tasks on your Big Data Extensions deployment.

A status of off means maintenance mode has been turned off, and it is not safe to perform maintenance tasks such as backup and restore.

A status of on means Big Data Extensions has entered maintenance mode, but it is not yet safe to perform back and restore operations. You must wait until the system returns the safe status message.

3 Verify that Big Data Extensions is in maintenance mode.

When Big Data Extensions completes all jobs that have been submitted, the maintenance status will enter safe mode. Run the serengeti-maintenance.sh with the status parameter repeatedly until it returns the safe system status message.

serengeti-maintenance.sh status safe

4 Perform the necessary system maintenance tasks.

5 Once you have completed the necessary system maintenance tasks, return Big Data Extensions to its

normal operating state by manually exiting maintenance mode.

serengeti-maintenance.sh off

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Monitoring the Big Data Extensions

Environment 14

You can monitor the status of Serengeti-deployed clusters, including their datastores, networks, and resource pools through the Serengeti Command-Line Interface. You can also view a list of available Hadoop distributions. Monitoring capabilities are also available in the vSphere Web Client.

This chapter includes the following topics:

“View List of Application Managers by using the Serengeti Command-Line Interface,” on page 91

“View Available Hadoop Distributions with the Serengeti Command-Line Interface,” on page 92

“View Supported Distributions for All Application Managers by Using the Serengeti Command-Line

Interface,” on page 92

“View Configurations or Roles for Application Manager and Distribution by Using the Serengeti

Command-Line Interface,” on page 92

“View Provisioned Clusters with the Serengeti Command-Line Interface,” on page 93

“View Datastores with the Serengeti Command-Line Interface,” on page 93

“View Networks with the Serengeti Command-Line Interface,” on page 93

“View Resource Pools with the Serengeti Command-Line Interface,” on page 94

View List of Application Managers by using the Serengeti CommandLine Interface

You can use the appManager list command to list the application managers that are installed on the Big Data Extensions environment.

Prerequisites

Verify that you are connected to an application manager.

Procedure

1 Access the Serengeti CLI.

2 Run the appmanager list command.

appmanager list

The command returns a list of all application managers that are installed on the Big Data Extensions environment.

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View Available Hadoop Distributions with the Serengeti CommandLine Interface

Supported distributions are those distributions that are supported by Big Data Extensions. Available distributions are those distributions that have been added into your Big Data Extensions environment. You use the distro list command to view a list of Hadoop distributions that are available in your Serengeti deployment. When you create clusters, you can use any available Hadoop distribution.

Procedure

1 Access the Serengeti CLI.

2 Run the distro list command.

The available Hadoop distributions are listed, along with their packages.

What to do next

Before you use a distribution, verify that it includes the services that you want to deploy. If services are missing, add the appropriate packages to the distribution.

View Supported Distributions for All Application Managers by Using the Serengeti Command-Line Interface

Prerequisites

Verify that you are connected to an application manager.

Procedure

1 Access the Serengeti CLI.

2 Run the appmanager list command.

appmanager list --name application_manager_name [--distros]

If you do not include the --name parameter, the command returns a list of all the Hadoop distributions that are supported on each of the application managers in the Big Data Extensions environment.

The command returns a list of all distributions that are supported for the application manager of the name that you specify.

View Configurations or Roles for Application Manager and Distribution by Using the Serengeti Command-Line Interface

You can use the appManager list command to list the Hadoop configurations or roles for a specific application manager and distribution.

The configuration list includes those configurations that you can use to configure the cluster in the cluster specifications.

The role list contains the roles that you can use to create a cluster. You should not use unsupported roles to create clusters in the application manager.

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Prerequisites

Verify that you are connected to an application manager.

Procedure

1 Access the Serengeti CLI.

2 Run the appmanager list command.

appmanager list --name application_manager_name [--distro distro_name (--configurations | --roles) ]

The command returns a list of the Hadoop configurations or roles for a specific application manager and distribution.

View Provisioned Clusters with the Serengeti Command-Line Interface

From the Serengeti CLI, you can list the provisioned clusters that are in the Serengeti deployment.

Procedure

1 Access the Serengeti CLI.

2 Run the cluster list command.

cluster list

This example displays a specific cluster by including the --name parameter.

cluster list --name cluster_name

This example displays detailed information about a specific cluster by including the --name and --

detail parameters.

cluster list --name cluster_name –-detail

View Datastores with the Serengeti Command-Line Interface

From the Serengeti CLI, you can see the datastores that are in the Serengeti deployment.

Procedure

1 Access the Serengeti CLI.

2 Run the datastore list command.

This example displays detailed information by including the --detail parameter.

datastore list --detail

This example displays detailed information about a specific datastore by including the --name and --

detail parameters.

datastore list --name datastore_name --detail

View Networks with the Serengeti Command-Line Interface

From the Serengeti CLI, you can see the networks that are in the Serengeti deployment.

Procedure

1 Access the Serengeti CLI.

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2 Run the network list command.

This example displays detailed information by including the --detail parameter.

network list --detail

This example displays detailed information about a specific network by including the --name and --

detail parameters.

network list --name network_name --detail

View Resource Pools with the Serengeti Command-Line Interface

From the Serengeti CLI, you can see the resource pools that are in the Serengeti deployment.

Procedure

1 Access the Serengeti CLI.

2 Run the resourcepool list command.

This example displays detailed information by including the --detail parameter.

resourcepool list --detail

This example displays detailed information about a specific datastore by including the --name and --

detail parameters.

resourcepool list --name resourcepool_name –-detail

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Cluster Specification Reference 15

To customize your clusters, you must know how to use Serengeti cluster specification files and define the cluster requirements with the various attributes and objects. After you create your configuration files you can convert them to JSON file format.

This chapter includes the following topics:

“Cluster Specification File Requirements,” on page 95

“Cluster Definition Requirements,” on page 95

“Annotated Cluster Specification File,” on page 96

“Cluster Specification Attribute Definitions,” on page 99

“White Listed and Black Listed Hadoop Attributes,” on page 101

“Convert Hadoop XML Files to Serengeti JSON Files,” on page 103

Cluster Specification File Requirements

A cluster specification file is a text file with the configuration attributes provided in a JSON-like formatted structure. Cluster specification files must adhere to requirements concerning syntax, quotation mark usage, and comments.

To parse cluster specification files, Serengeti uses the Jackson JSON Processor. For syntax requirements,

such as the truncation policy for float types, see the Jackson JSON Processor Wiki.

Always enclose digital values in quotation marks. For example:

"mapred.tasktracker.reduce.tasks.maximum" : "2"

The quotation marks ensure that integers are correctly interpreted instead of being converted to doubleprecision floating point, which can cause unintended consequences.

Do not include any comments.

Cluster Definition Requirements

Cluster specification files contain configuration definitions for clusters, such as their roles and node groups. Cluster definitions must adhere to requirements concerning node group roles, cluster roles, and instance numbers.

A cluster definition has the following requirements:

Node group roles cannot be empty. You can determine the valid role names for your Hadoop

distribution by using the distro list command.

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The hadoop_namenode and hadoop_jobtracker roles must be configured in a single node group.

In Hadoop 2.0 clusters, such as CDH4 or Pivotal HD, the instance number can be greater than 1 to

create an HDFS HA or Federation cluster.

Otherwise, the total instance number must be 1.

Node group instance numbers must be positive numbers.

Annotated Cluster Specification File

The Serengeti cluster specification file defines the nodes, resources, and so on for a cluster. You can use this annotated cluster specification file, and the sample files in /opt/serengeti/samples, as models when you create your clusters.

The following code is a typical cluster specification file. For code annotations, see Table 15-1.

1 { 2 "nodeGroups" : [ 3 { 4 "name": "master", 5 "roles": [ 6 "hadoop_namenode", 7 "hadoop_jobtracker" 8 ], 9 "instanceNum": 1, 10 "instanceType": "LARGE", 11 "cpuNum": 2, 12 "memCapacityMB":4096, 13 "storage": { 14 "type": "SHARED", 15 "sizeGB": 20 16 }, 17 "haFlag":"on", 18 "rpNames": [ 19 "rp1" 20 ] 21 }, 22 { 23 "name": "data", 24 "roles": [ 25 "hadoop_datanode" 26 ], 27 "instanceNum": 3, 28 "instanceType": "MEDIUM", 29 "cpuNum": 2, 30 "memCapacityMB":2048, 31 "storage": { 32 "type": "LOCAL", 33 "sizeGB": 50, 34 "dsNames4Data": ["DSLOCALSSD"], 35 "dsNames4System": ["DSNDFS"] 36 } 37 "placementPolicies": { 38 "instancePerHost": 1, 39 "groupRacks": { 40 "type": "ROUNDROBIN", 41 "racks": ["rack1", "rack2", "rack3"]

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42 } 43 } 44 }, 45 { 46 "name": "compute", 47 "roles": [ 48 "hadoop_tasktracker" 49 ], 50 "instanceNum": 6, 51 "instanceType": "SMALL", 52 "cpuNum": 2, 53 "memCapacityMB":2048, 54 "storage": { 55 "type": "LOCAL", 56 "sizeGB": 10 57 } 58 "placementPolicies": { 59 "instancePerHost": 2, 60 "groupAssociations": [{ 61 "reference": "data", 62 "type": "STRICT" 63 }] 64 } 65 }, 66 { 67 "name": "client", 68 "roles": [ 69 "hadoop_client", 70 "hive", 71 "hive_server", 72 "pig" 73 ], 74 "instanceNum": 1, 75 "instanceType": "SMALL", 76 "memCapacityMB": 2048, 77 "storage": { 78 "type": "LOCAL", 79 "sizeGB": 10, 80 "dsNames": [“ds1”, “ds2”] 81 } 82 } 83 ], 84 "configuration": { 85 } 86 }

Chapter 15 Cluster Specification Reference

The cluster definition elements are defined in the table.

Table 15‑1. Example Cluster Specification Annotation

Line(s) Attribute Example Value Description

4 name master Node group name.

5-8 role hadoop_namenode,

hadoop_jobtracker

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Table 15‑1. Example Cluster Specification Annotation (Continued)

Line(s) Attribute Example Value Description

9 instanceNum 1 Number of instances in the node group.

10 instanceType LARGE Node group instance type.

11 cpuNum 2 Number of CPUs per virtual machine.

12 memCapacityMB 4096 RAM size, in MB, per virtual machine.

13-16 storage See lines 14-15 for one

group's storage attributes

14 type SHARED Storage type.

15 sizeGB 20 Storage size.

17 haFlag on HA protection for the node group.

18-20 rpNames rp1 Resource pools under which the node group

22-36 Node group

definition for the data node

37-44 placementPolicies See code sample Data node group's placement policy constraints.

Only one virtual machine is created for the group.

You can have multiple instances for

hadoop_tasktracker, hadoop_datanode, hadoop_client, pig, and hive.

For HDFS1 clusters, you can have only one

instance of hadoop_namenode and hadoop_jobtracker.

For HDFS2 clusters, you can have two

hadoop_namenode instances.

With a MapR distribution, you can configure

multiple instances of hadoop_jobtracker.

Instance types are predefined virtual machine specifications, which are combinations of the number of CPUs, RAM sizes, and storage size. The predefined numbers can be overridden by the cpuNum, memCapacityMB, and storage attributes in the Serengeti server specification file.

This attribute overrides the number of vCPUs in the predefined virtual machine specification.

This attribute overrides the RAM size in the predefined virtual machine specification.

Node group storage requirements.

The node group is deployed using only shared storage.

Each node in the node group is deployed with 20GB available disk space.

The node group is deployed with vSphere HA protection.

virtual machines are deployed. These pools can be an array of values.

See lines 3-21, which define the same attributes for the master node.

In lines 34-35, data disks are placed on dsNames4Data datastores, and system disks are placed on dsNames4System datastores.

You need at least three ESXi hosts because there are three instances and a requirement that each instance be on its own host. This group is provisioned on hosts on rack1, rack2, and rack3 by using a ROUNDROBIN algorithm.

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Table 15‑1. Example Cluster Specification Annotation (Continued)

Line(s) Attribute Example Value Description

45-57 Node group

definition for the compute node

58-65 placementPolicies See code sample Compute node group's placement policy

66-82 Node group

definition for the client node

83-86 configuration Empty in the code

sample

Cluster Specification Attribute Definitions

Chapter 15 Cluster Specification Reference

See lines 4-16, which define the same attributes for the master node.

constraints.

You need at least three ESXi hosts to meet the instance requirements. The compute node group references a data node group through STRICT typing. The two compute instances use a data instance on the ESXi host. The STRICT association provides better performance.

See previous node group definitions.

Hadoop configuration customization.

Cluster definitions include attributes for the cluster itself and for each of the node groups.

Cluster Specification Outer Attributes

Cluster specification outer attributes apply to the cluster as a whole.

Table 15‑2. Cluster Specification Outer Attributes

Attribute Type Mandatory/ Optional Description

nodeGroups object Mandatory One or more group specifications. See Table 15-3.

configuration object Optional Customizable Hadoop configuration key/value pairs.

externalHDFS string Optional Valid only for compute-only clusters. URI of external HDFS.

Cluster Specification Node Group Objects and Attributes

Node group objects and attributes apply to one node group in a cluster.

Table 15‑3. Cluster Specification’s Node Group Objects and Attributes

Mandatory/

Attribute Type

name string Mandatory User defined node group name.

roles list of string Mandatory List of software packages or services to install on the virtual

Optional Description

machine. Values must match the roles displayed by the distro list command.

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Table 15‑3. Cluster Specification’s Node Group Objects and Attributes (Continued)

Mandatory/

Attribute Type

instanceNumber integer Mandatory Number of virtual machines in the node group:

instanceType string Optional Size of virtual machines in the node group, expressed as the name

cpuNum integer Optional

memCapacityMB integer Optional RAM size, in MB, per virtual machine.

Storage object Optional Storage settings.

type string Optional Storage type:

sizeGB integer Optional Data storage size. Must be a positive integer.

dsNames list of string Optional Array of datastores the node group can use.

dnNames4Data list of string Optional Array of datastores the data node group can use.

dsNames4System list of string Optional Array of datastores the system can use.

rpNames list of string Optional Array of resource pools the node group can use.

haFlag string Optional By default, NameNode and JobTracker nodes are protected by

placementPolicies object Optional Up to three optional constraints:

Optional Description

Positive integer.

Generally, you can have multiple instances for

hadoop_tasktracker, hadoop_datanode, hadoop_client, pig, and hive.

For HDFS1 clusters, you can have only one instance of

hadoop_namenode and hadoop_jobtracker.

For HDFS2 clusters, you can have two hadoop_namenode instances.

With a MapR distribution, you can configure multiple

instances of hadoop_jobtracker.

of a predefined virtual machine template. See Table 15-4.

SMALL

MEDIUM

LARGE

EXTRA_LARGE

If you specify cpuNum , memCapacityMB , or sizeGB attributes, they override the corresponding value of your selected virtual machine template for the applicable node group.

Number of CPUs per virtual machine. If the haFlag value is FT, the cpuNum value must be 1.

NOTE When using MapR 3.1, you must specify a minimum of 5120 MBs of memory capacity for the zookeeper, worker, and client nodes.

LOCAL. For local storage

SHARED. For shared storage.

vSphere HA.

on. Protect the node with vSphere HA.

ft. Protect the node with vSphere FT.

off. Do not use vSphere HA or vSphere FT.

instancePerHost

groupRacks

groupAssociations

100 VMware, Inc.

VMware vSphere Big Data Extensions - 2.2 User’s Manual

Specifications and Main Features

Frequently Asked Questions

User Manual

Contents

About This Book

Access the Serengeti CLI By Using the Remote CLI Client

Log in to Hadoop Nodes with the Serengeti Command-Line Interface Client

Managing Application Managers 2

About Application Managers 3

About Application Managers

Add an Application Manager by Using the Serengeti Command-Line Interface

Modify an Application Manager by Using the Serengeti Command-Line Interface

View Supported Distributions for All Application Managers by Using the Serengeti Command-Line Interface

View Configurations or Roles for Application Manager and Distribution by Using the Serengeti Command-Line Interface

View List of Application Managers by using the Serengeti Command-Line Interface

Delete an Application Manager by Using the Serengeti Command-Line Interface

Add a Resource Pool with the Serengeti Command-Line Interface

Remove a Resource Pool with the Serengeti Command-Line Interface

Add a Datastore with the Serengeti Command-Line Interface

Remove a Datastore with the Serengeti Command-Line Interface

Add a Network with the Serengeti Command-Line Interface

Remove a Network with the Serengeti Command-Line Interface

Reconfigure a Static IP Network with the Serengeti Command-Line Interface

Reconfigure the DNS Type and Generate a Hostname with the Serengeti Command-Line Interface

Increase Cloning Performance and Resource Usage of Virtual Machines

Managing Users and User Accounts 11

Create an LDAP Service Configuration File Using the Serengeti Command-Line Interface

Activate Centralized User Management Using the Serengeti Command-Line Interface

Create a Cluster With LDAP User Authentication Using the Serengeti Command-Line Interface

Change User Management Modes Using the Serengeti Command-Line Interface

Modify LDAP Configuration Using the Serengeti Command-Line Interface

Creating Hadoop and HBase Clusters 12

About Hadoop and HBase Cluster Deployment Types

Default Hadoop Cluster Configuration for Serengeti

Hadoop Distributions Supporting MapReduce v1 and MapReduce v2 (YARN)

Default HBase Cluster Configuration for Serengeti

About Cluster Topology

Topology Rack-Hosts Mapping File

Topology Placement Policy Definition Files

Create a Cluster with Topology Awareness with the Serengeti Command-Line Interface

About HBase Clusters

Create a Default HBase Cluster by Using the Serengeti Command-Line Interface

Create an HBase Only Cluster in Big Data Extensions

Prerequisites for Creating an HBase Only Cluster

Prepare the EMC Isilon OneFS as the External HDFS Cluster

Create an HBase Only Cluster by Using the Serengeti Command-Line Interface

Create an HBase Cluster with vSphere HA Protection with the Serengeti Command-Line Interface

Create an HBase Only Cluster with External Namenode HA HDFS Cluster

About MapReduce Clusters

Create a MapReduce v2 (YARN) Cluster by Using the Serengeti Command-Line Interface

Create a MapReduce v1 Worker Only Cluster with External Namenode HA HDFS Cluster

Create a MapReduce v2 Worker Only Cluster with External Namenode HA HDFS Cluster

About Data Compute Clusters

Create a Data-Compute Separated Cluster with Topology Awareness and Placement Constraints

Create a Data-Compute Separated Cluster with No Node Placement Constraints

Create a Data-Compute Separated Cluster with Placement Policy Constraints

Create a Compute-Only Cluster with the Default Application Manager

Create a Compute-Only Cluster with the Cloudera Manager Application Manager

Create a Compute Workers Only Cluster With Non-Namenode HA HDFS Cluster

About Customized Clusters

Create a Default Serengeti Hadoop Cluster with the Serengeti Command-Line Interface

Create a Basic Cluster with the Serengeti Command-Line Interface

Create a Cluster with an Application Manager by Using the Serengeti Command-Line Interface

Create a Compute Workers Only Cluster by Using the vSphere Web Client

Create a Cluster with a Custom Administrator Password with the Serengeti Command-Line Interface

Create a Cluster with Multiple Networks with the Serengeti Command-Line Interface

Create a Cluster with Assigned Resources with the Serengeti Command-Line Interface

Create a Cluster with Any Number of Master, Worker, and Client Nodes

Stop and Start a Cluster with the Serengeti Command-Line Interface

Scale Out a Cluster with the Serengeti Command-Line Interface

Scale CPU and RAM with the Serengeti Command-Line Interface

Reconfigure a Cluster with the Serengeti Command-Line Interface

About Resource Usage and Elastic Scaling

Enable Elastic Scaling for a Hadoop Cluster with the Serengeti Command-Line Interface

Enable Manual Scaling for a Hadoop Cluster with the Serengeti Command-Line Interface

Configure Scaling Parameters with the Serengeti Command-Line Interface

Schedule Fixed Elastic Scaling for a Hadoop Cluster

Delete a Cluster by Using the Serengeti Command-Line Interface

About vSphere High Availability and vSphere Fault Tolerance